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Zur Analyse von Lippenbewegungsabläufen wird ein aktives Konturmodell eingesetzt. Probleme bereitet die hohe Sprechgeschwindigkeit, die in star ken Objektverschiebungen result iert und bislang nicht durch eine alleinige Konturanpassung kompensiert werden kann. In diesem Beitrag werden die klassischen aktiven Konturmodelle um eine Vorjustierung der Grobkonturen erweitert, die eine energiebasierte Konturanpassung erst möglich macht. Die Schätzung der Verschiebung zur Vorjustierung basiert auf dem Gradientenbild und einem prädikatenlogisch formulierten Regelwerk, das Annahmen und Nebenbedingungen als Wissensbasis enthält. Mit Hilfe dieser Erweiterungen ist eine automatisierte Konturverfolgung der Lippen möglich.
Whole-body PET/CT imaging
(2008)
Aim
Combined whole-body (WB) PET/CT imaging provides better overall co-registration compared to separate CT and PET. However, in clinical routine local PET-CT mis-registration cannot be avoided. Thus, the reconstructed PET tracer distribution may be biased when using the misaligned CT transmission data for CT-based attenuation correction (CT-AC). We investigate the feasibility of retrospective co-registration techniques to align CT and PET images prior to CT-AC, thus improving potentially the quality of combined PET/CT imaging in clinical routine.
Methods
First, using a commercial software registration package CT images were aligned to the uncorrected PET data by rigid and non-rigid registration methods. Co-registration accuracy of both alignment approaches was assessed by reviewing the PET tracer uptake patterns (visual, linked cursor display) following attenuation correction based on the original and co-registered CT. Second, we investigated non-rigid registration based on a prototype ITK implementation of the B-spline algorithm on a similar targeted MR-CT registration task, there showing promising results.
Results
Manual rigid, landmark-based co-registration introduced unacceptable misalignment, in particular in peripheral areas of the whole-body images. Manual, non-rigid landmark-based co-registration prior to CT-AC was successful with minor loco-regional distortions. Nevertheless, neither rigid nor non-rigid automatic co-registration based on the Mutual Information image to image metric succeeded in co-registering the CT and noAC-PET images. In contrast to widely available commercial software registration our implementation of an alternative automated, non-rigid B-spline co-registration technique yielded promising results in this setting with MR-CT data.
Conclusion
In clinical PET/CT imaging, retrospective registration of CT and uncorrected PET images may improve the quality of the AC-PET images. As of today no validated and clinically viable commercial registration software is in routine use. This has triggered our efforts in pursuing new approaches to a validated, non-rigid co-registration algorithm applicable to whole-body PET/CT imaging of which first results are presented here. This approach appears suitable for applications in retrospective WB-PET/CT alignment.
Ziel
Kombinierte PET/CT-Bildgebung ermöglicht verbesserte Koregistrierung von PET- und CT-Daten gegenüber separat akquirierten Bildern. Trotzdem entstehen in der klinischen Anwendung lokale Fehlregistrierungen, die zu Fehlern in der rekonstruierten PET- Tracerverteilung führen können, falls die unregistrierten CT-Daten zur Schwächungskorrektur (AC) der Emissionsdaten verwendet werden. Wir untersuchen daher die Anwendung von Bildregistrierungsalgorithmen vor der CT-basierten AC zur Verbesserung der PET-Aufnahmen.
Methoden
Mittels einer kommerziellen Registrierungssoftware wurden die CT-Daten eines PET/CT- Tomographen durch landmarken- und intensitätsbasierte rigide (starre) und nicht-rigide Registrierungsverfahren räumlich an die unkorrigierten PET-Emissionsdaten angepasst und zur AC verwendet. Zur Bewertung wurden die Tracerverteilungen in den PET-Bildern (vor AC, CT-AC, CT-AC nach Koregistrierung) visuell und mit Hilfe korrelierter Fadenkreuze verglichen. Zusätzlich untersuchten wir die ITK-Implementierung der bekannten B-spline basierten, nicht-rigiden Registrierungsansätze im Hinblick auf ihre Verwendbarkeit für die multimodale PET/CT-Ganzkörperregistrierung.
Ergebnisse
Mittels landmarkenbasierter, nicht-rigider Registrierung konnte die Tracerverteilung in den PET-Daten lokal verbessert werden. Landmarkenbasierte rigide Registrierung führte zu starker Fehlregistrierung in entfernten Körperregionen. Automatische rigide und nicht-rigide Registrierung unter Verwendung der Mutual-Information-Ähnlichkeitsmetrik versagte auf allen verwendeten Datensätzen. Die automatische Registrierung mit B-spline-Funktionen zeigte vielversprechende Resultate in der Anwendung auf einem ähnlich gelagerten CT–MR-Registrierungsproblem.
Fazit
Retrospektive, nicht-rigide Registrierung unkorrigierter PET- und CT-Aufnahmen aus kombinierten Aufnahmensystemen vor der AC kann die Qualität von PET-Aufnahmen im klinischen Einsatz verbessern. Trotzdem steht bis heute im klinischen Alltag keine validierte, automatische Registrierungssoftware zur Verfügung. Wir verfolgen dazu Ansätze für validierte, nicht-rigide Bildregistrierung für den klinischen Einsatz und präsentieren erste Ergebnisse.
Medical image processing provides core innovation for medical imaging. This paper is focused on recent developments from science to applications analyzing the past fifteen years of history of the proceedings of the German annual meeting on medical image processing (BVM). Furthermore, some members of the program committee present their personal points of views: (i) multi-modality for imaging and diagnosis, (ii) analysis of diffusion-weighted imaging, (iii) model-based image analysis, (iv) registration of section images, (v) from images to information in digital endoscopy, and (vi) virtual reality and robotics. Medical imaging and medical image computing is seen as field of rapid development with clear trends to integrated applications in diagnostics, treatment planning and treatment.
In this study, we aimed to develop an artificial intelligence clinical decision support solution to mitigate operator-dependent limitations during complex endoscopic procedures such as endoscopic submucosal dissection and peroral endoscopic myotomy, for example, bleeding and perforation. A DeepLabv3-based model was trained to delineate vessels, tissue structures and instruments on endoscopic still images from such procedures. The mean cross-validated Intersection over Union and Dice Score were 63% and 76%, respectively. Applied to standardised video clips from third-space endoscopic procedures, the algorithm showed a mean vessel detection rate of 85% with a false-positive rate of 0.75/min. These performance statistics suggest a potential clinical benefit for procedure safety, time and also training.
Einleitung Die Endoskopische Retrograde Cholangiopankreatikographie (ERCP) ist der Goldstandard in der Diagnostik und Therapie von Erkrankungen des pankreatobiliären Trakts. Jedoch ist sie technisch sehr anspruchsvoll und weist eine vergleichsweise hohe Komplikationsrate auf.
Ziele
In der vorliegenden Machbarkeitsstudie soll geprüft werden, ob mithilfe eines Deep-learning-Algorithmus die Papille und das Ostium zuverlässig detektiert werden können und somit für Endoskopiker mit geringer Erfahrung ein geeignetes Hilfsmittel, insbesondere für die Ausbildungssituation, darstellen könnten.
Methodik
Wir betrachteten insgesamt 606 Bilddatensätze von 65 Patienten. In diesen wurde sowohl die Papilla duodeni major als auch das Ostium segmentiert. Anschließend wurde eine neuronales Netz mittels eines Deep-learning-Algorithmus trainiert. Außerdem erfolgte eine 5-fache Kreuzvaldierung.
Ergebnisse
Bei einer 5-fachen Kreuzvaldierung auf den 606 gelabelten Daten konnte für die Klasse Papille eine F1-Wert von 0,7908, eine Sensitivität von 0,7943 und eine Spezifität von 0,9785 erreicht werden, für die Klasse Ostium eine F1-Wert von 0,5538, eine Sensitivität von 0,5094 und eine Spezifität von 0,9970 (vgl. [Tab. 1]). Unabhängig von der Klasse zeigte sich gemittelt (Klasse Papille und Klasse Ostium) ein F1-Wert von 0,6673, eine Sensitivität von 0,6519 und eine Spezifität von 0,9877 (vgl. [Tab. 2]).
Schlussfolgerung
In vorliegende Machbarkeitsstudie konnte das neuronale Netz die Papilla duodeni major mit einer hohen Sensitivität und sehr hohen Spezifität identifizieren. Bei der Detektion des Ostiums war die Sensitivität deutlich geringer. Zukünftig soll das das neuronale Netz mit mehr Daten trainiert werden. Außerdem ist geplant, den Algorithmus auch auf Videos anzuwenden. Somit könnte langfristig ein geeignetes Hilfsmittel für die ERCP etabliert werden.
Utility of Smartphone-based Three-dimensional Surface Imaging for Digital Facial Anthropometry
(2024)
Background
The utilization of three-dimensional (3D) surface imaging for facial anthropometry is a significant asset for patients undergoing maxillofacial surgery. Notably, there have been recent advancements in smartphone technology that enable 3D surface imaging.
In this study, anthropometric assessments of the face were performed using a smartphone and a sophisticated 3D surface imaging system.
Methods
30 healthy volunteers (15 females and 15 males) were included in the study. An iPhone 14 Pro (Apple Inc., USA) using the application 3D Scanner App (Laan Consulting Corp., USA) and the Vectra M5 (Canfield Scientific, USA) were employed to create 3D surface models. For each participant, 19 anthropometric measurements were conducted on the 3D surface models. Subsequently, the anthropometric measurements generated by the two approaches were compared. The statistical techniques employed included the paired t-test, paired Wilcoxon signed-rank test, Bland–Altman analysis, and calculation of the intraclass correlation coefficient (ICC).
Results
All measurements showed excellent agreement between smartphone-based and Vectra M5-based measurements (ICC between 0.85 and 0.97). Statistical analysis revealed no statistically significant differences in the central tendencies for 17 of the 19 linear measurements. Despite the excellent agreement found, Bland–Altman analysis revealed that the 95% limits of agreement between the two methods exceeded ±3 mm for the majority of measurements.
Conclusion
Digital facial anthropometry using smartphones can serve as a valuable supplementary tool for surgeons, enhancing their communication with patients. However, the proposed data suggest that digital facial anthropometry using smartphones may not yet be suitable for certain diagnostic purposes that require high accuracy.
Objectives: C-11-methionine (MET) is particularly useful in brain tumor diagnosis but unspecific uptake e.g. in cerebral ischemia has been reported (1). The F-18-labeled amino acid O-(2-[F-18]fluoroethyl)-L-tyrosine (FET) shows a similar clinical potential as MET in brain tumor diagnosis but is applicable on a wider clinical scale. The aim of this study was to evaluate the uptake of FET and H-3-MET in focal cortical ischemia in rats by dual tracer autoradiography.
Methods: Focal cortical ischemia was induced in 12 Fisher CDF rats using the photothrombosis model (PT). One day (n=3) , two days (n=5) and 7 days (n=4) after induction of the lesion FET and H-3-MET were injected intravenously. One hour after tracer injection animals were killed, the brains were removed immediately and frozen in 2-methylbutane at -50°C. Brains were cut in coronal sections (thickness: 20 µm) and exposed first to H-3 insensitive photoimager plates to measure FET distribution. After decay of F-18 the distribution of H-3-MET was determined. The autoradiograms were evaluated by regions of interest (ROIs) placed on areas with increased tracer uptake in the PT and the contralateral brain. Lesion to brain ratios (L/B) were calculated by dividing the mean uptake in the lesion and the brain. Based on previous studies in gliomas a L/B ratio > 1.6 was considered as pathological for FET.
Results: Variable increased uptake of both tracers was observed in the PT and its demarcation zone at all stages after PT. The cut-off level of 1.6 for FET was exceeded in 9/12 animals. One day after PT the L/B ratios were 2.0 ± 0.6 for FET vs. 2.1 ± 1.0 for MET (mean ± SD); two days after lesion 2.2 ± 0.7 for FET vs. 2.7 ± 1.0 for MET and 7 days after lesion 2.4 ± 0.4 for FET vs. 2.4 ± 0.1 for MET. In single cases discrepancies in the uptake pattern of FET and MET were observed.
Conclusions: FET like MET may exhibit significant uptake in infarcted areas or the immediate vincinity which has to be considered in the differential diagnosis of unkown brain lesions. The discrepancies in the uptake pattern of FET and MET in some cases indicates either differences in the transport mechanisms of both amino acids or a different affinity for certain cellular components.
Zur diagnostischen Unterstützung bei der Befundung laryngealer Erkrankungen soll eine Farb- und Formanalyse der Stimmlippen durchgeführt werden. In diesem Beitrag wird ein Verfahren zur Trennung der spiegelnden und diffusen Reflexionsanteile in Farbbildern des Larynx vorgestellt. Die Farbe der diffusen Komponente entspricht dabei der beleuchtungsunabhängigen Objektfarbe, während deren Wichtungsfaktoren als Eingabe für Shape-from-Shading-Verfahren zur Oberflächenrekonstruktion dienen.
Polarised light imaging (PLI) utilises the birefringence of the myelin sheaths in order to visualise the orientation of nerve fibres in microtome sections of adult human post-mortem brains at ultra-high spatial resolution. The preparation of post-mortem brains for PLI involves fixation, freezing and cutting into 100-μm-thick sections. Hence, geometrical distortions of histological sections are inevitable and have to be removed for 3D reconstruction and subsequent fibre tracking. We here present a processing pipeline for 3D reconstruction of these sections using PLI derived multimodal images of post-mortem brains. Blockface images of the brains were obtained during cutting; they serve as reference data for alignment and elimination of distortion artefacts. In addition to the spatial image transformation, fibre orientation vectors were reoriented using the transformation fields, which consider both affine and subsequent non-linear registration. The application of this registration and reorientation approach results in a smooth fibre vector field, which reflects brain morphology. PLI combined with 3D reconstruction and fibre tracking is a powerful tool for human brain mapping. It can also serve as an independent method for evaluating in vivo fibre tractography.
Background:
Reliable, time- and cost-effective, and clinician-friendly diagnostic tools are cornerstones in facial palsy (FP) patient management. Different automated FP grading systems have been developed but revealed persisting downsides such as insufficient accuracy and cost-intensive hardware. We aimed to overcome these barriers and programmed an automated grading system for FP patients utilizing the House and Brackmann scale (HBS).
Methods:
Image datasets of 86 patients seen at the Department of Plastic, Hand, and Reconstructive Surgery at the University Hospital Regensburg, Germany, between June 2017 and May 2021, were used to train the neural network and evaluate its accuracy. Nine facial poses per patient were analyzed by the algorithm.
Results:
The algorithm showed an accuracy of 100%. Oversampling did not result in altered outcomes, while the direct form displayed superior accuracy levels when compared to the modular classification form (n = 86; 100% vs. 99%). The Early Fusion technique was linked to improved accuracy outcomes in comparison to the Late Fusion and sequential method (n = 86; 100% vs. 96% vs. 97%).
Conclusions:
Our automated FP grading system combines high-level accuracy with cost- and time-effectiveness. Our algorithm may accelerate the grading process in FP patients and facilitate the FP surgeon’s workflow.
Time-Dependent Joint Probability Speed Function for Level-Set Segmentation of Rat-Brain Slices
(2008)
The segmentation of rat brain slices suffers from illumination inhomogeneities and staining effects. State-of-the-art level-set methods model slice and background with intensity mixture densities defining the speed function as difference between the respective probabilites. Nevertheless, the overlap of these distributions causes an inaccurate stopping at the slice border. In this work, we propose the characterisation of the border area with intensity pairs for inside and outside estimating joint intensity probabilities. Method - In contrast to global object and background models, we focus on the object border characterised by a joint mixture density. This specifies the probability of the occurance of an inside and an outside value in direct adjacency. These values are not known beforehand, because inside and outside depend on the level-set evolution and change during time. Therefore, the speed function is computed time-dependently at the position of the current zero level-set. Along this zero level-set curve, the inside and outside values are derived as mean along the curvature normal directing inside and outside the object. Advantage of the joint probability distribution is to resolve the distribution overlaps, because these are assumed to be not located at the same border position. Results - The novel time-dependent joint probability based speed function is compared expermimentally with single probability based speed functions. Two rat brains with about 40 slices are segmented and the results analysed using manual segmentations and the Tanimoto overlap measure. Improved results are recognised for both data sets.
The Impact of Semi-Automated Segmentation and 3D Analysis on Testing New Osteosynthesis Material
(2017)
A new protocol for testing osteosynthesis material postoperatively combining semi-automated segmentation and 3D analysis of surface meshes is proposed. By various steps of transformation and measuring, objective data can be collected. In this study the specifications of a locking plate used for mediocarpal arthrodesis of the wrist were examined. The results show, that union of the lunate, triquetrum, hamate and capitate was achieved and that the plate is comparable to coexisting arthrodesis systems. Additionally, it was shown, that the complications detected correlate to the clinical outcome. In synopsis, this protocol is considered beneficial and should be taken into account in further studies.
Local gray level dependencies of natural images can be modelled by means of co-occurrence matrices containing joint probabilities of gray-level pairs. Texture, however, is a resolution-dependent phenomenon and hence, classification depends on the chosen scale. Since there is no optimal scale for all textures we employ a multiscale approach that acquires textural features at several scales. Thus linear and nonlinear scale-spaces are analyzed by multiscale co-occurrence matrices that describe the statistical behavior of a texture in scale-space. Classification is then performed on the basis of texture features taken from the individual scale with the highest discriminatory power. By considering cross-scale occurrences of gray level pairs, the impact of filters on the feature is described and used for classification of natural textures. This novel method was found to improve classification rates of the common co-occurrence matrix approach on standard textures significantly.
The success of artificial intelligence in medicine is based on the need for large amounts of high quality training data. Sharing of medical image data, however, is often restricted by laws such as doctor-patient confidentiality. Although there are publicly available medical datasets, their quality and quantity are often low. Moreover, datasets are often imbalanced and only represent a fraction of the images generated in hospitals or clinics and can thus usually only be used as training data for specific problems. The introduction of generative adversarial networks (GANs) provides a mean to generate artificial images by training two convolutional networks. This paper proposes a method which uses GANs trained on medical images in order to generate a large number of artificial images that could be used to train other artificial intelligence algorithms. This work is a first step towards alleviating data privacy concerns and being able to publicly share data that still contains a substantial amount of the information in the original private data. The method has been evaluated on several public datasets and quantitative and qualitative tests showing promising results.
Objective: Artificial intelligence (AI) may reduce underdiagnosed or overlooked upper GI (UGI) neoplastic and preneoplastic conditions, due to subtle appearance and low disease prevalence. Only disease-specific AI performances have been reported, generating uncertainty on its clinical value.
Design: We searched PubMed, Embase and Scopus until July 2020, for studies on the diagnostic performance of AI in detection and characterisation of UGI lesions. Primary outcomes were pooled diagnostic accuracy, sensitivity and specificity of AI. Secondary outcomes were pooled positive (PPV) and negative (NPV) predictive values. We calculated pooled proportion rates (%), designed summary receiving operating characteristic curves with respective area under the curves (AUCs) and performed metaregression and sensitivity analysis.
Results: Overall, 19 studies on detection of oesophageal squamous cell neoplasia (ESCN) or Barrett's esophagus-related neoplasia (BERN) or gastric adenocarcinoma (GCA) were included with 218, 445, 453 patients and 7976, 2340, 13 562 images, respectively. AI-sensitivity/specificity/PPV/NPV/positive likelihood ratio/negative likelihood ratio for UGI neoplasia detection were 90% (CI 85% to 94%)/89% (CI 85% to 92%)/87% (CI 83% to 91%)/91% (CI 87% to 94%)/8.2 (CI 5.7 to 11.7)/0.111 (CI 0.071 to 0.175), respectively, with an overall AUC of 0.95 (CI 0.93 to 0.97). No difference in AI performance across ESCN, BERN and GCA was found, AUC being 0.94 (CI 0.52 to 0.99), 0.96 (CI 0.95 to 0.98), 0.93 (CI 0.83 to 0.99), respectively. Overall, study quality was low, with high risk of selection bias. No significant publication bias was found.
Conclusion: We found a high overall AI accuracy for the diagnosis of any neoplastic lesion of the UGI tract that was independent of the underlying condition. This may be expected to substantially reduce the miss rate of precancerous lesions and early cancer when implemented in clinical practice.
Heavy smoke development represents an important challenge for operating physicians during laparoscopic procedures and can potentially affect the success of an intervention due to reduced visibility and orientation. Reliable and accurate recognition of smoke is therefore a prerequisite for the use of downstream systems such as automated smoke evacuation systems. Current approaches distinguish between non-smoked and smoked frames but often ignore the temporal context inherent in endoscopic video data. In this work, we therefore present a method that utilizes the pixel-wise displacement from randomly sampled images to the preceding frames determined using the optical flow algorithm by providing the transformed magnitude of the displacement as an additional input to the network. Further, we incorporate the temporal context at evaluation time by applying an exponential moving average on the estimated class probabilities of the model output to obtain more stable and robust results over time. We evaluate our method on two convolutional-based and one state-of-the-art transformer architecture and show improvements in the classification results over a baseline approach, regardless of the network used.
Polarized light imaging (PLI) enables the evaluation of fiber orientations in histological sections of human postmortem brains, with ultra-high spatial resolution. PLI is based on the birefringent properties of the myelin sheath of nerve fibers. As a result, the polarization state of light propagating through a rotating polarimeter is changed in such a way that the detected signal at each measurement unit of a charged-coupled device (CCD) camera describes a sinusoidal signal. Vectors of the fiber orientation defined by inclination and direction angles can then directly be derived from the optical signals employing PLI analysis. However, noise, light scatter and filter inhomogeneities interfere with the original sinusoidal PLI signals. We here introduce a novel method using independent component analysis (ICA) to decompose the PLI images into statistically independent component maps. After decomposition, gray and white matter structures can clearly be distinguished from noise and other artifacts. The signal enhancement after artifact rejection is quantitatively evaluated in 134 histological whole brain sections. Thus, the primary sinusoidal signals from polarized light imaging can be effectively restored after noise and artifact rejection utilizing ICA. Our method therefore contributes to the analysis of nerve fiber orientation in the human brain within a micrometer scale.
Pixel-level classification is an essential part of computer vision. For learning from labeled data, many powerful deep learning models have been developed recently. In this work, we augment such supervised segmentation models by allowing them to learn from unlabeled data. Our semi-supervised approach, termed Error-Correcting Supervision, leverages a collaborative strategy. Apart from the supervised training on the labeled data, the segmentation network is judged by an additional network. The secondary correction network learns on the labeled data to optimally spot correct predictions, as well as to amend incorrect ones. As auxiliary regularization term, the corrector directly influences the supervised training of the segmentation network. On unlabeled data, the output of the correction network is essential to create a proxy for the unknown truth. The corrector’s output is combined with the segmentation network’s prediction to form the new target. We propose a loss function that incorporates both the pseudo-labels as well as the predictive certainty of the correction network. Our approach can easily be added to supervised segmentation models. We show consistent improvements over a supervised baseline on experiments on both the Pascal VOC 2012 and the Cityscapes datasets with varying amounts of labeled data.
Chest X-Ray (CXR) images as part of a non-invasive diagnosis method are commonly used in today’s medical workflow. In traditional methods, physicians usually use their experience to interpret CXR images, however, there is a large interobserver variance. Computer vision may be used as a standard for assisted diagnosis. In this study, we applied an encoder-decoder neural network architecture for automatic lung region detection. We compared a three-class approach (left lung, right lung, background) and a two-class approach (lung, background). The differentiation of left and right lungs as direct result of a semantic segmentation on basis of neural nets rather than post-processing a lung-background segmentation is done here for the first time. Our evaluation was done on the NIH Chest X-ray dataset, from which 1736 images were extracted and manually annotated. We achieved 94:9% mIoU and 92% mIoU as segmentation quality measures for the two-class-model and the three-class-model, respectively. This result is very promising for the segmentation of lung regions having the simultaneous classification of left and right lung in mind.
In this paper, we address the problem of retrospective color shading correction. An extension of the established gray-level shading correction algorithm based on signal envelope (SE) estimation to color images is developed using principal color components. Compared to the probably most general shading correction algorithm based on entropy minimization, SE estimation does not need any computationally expensive optimization and thus can be implemented more effciently. We tested our new shading correction scheme on artificial as well as real endoscopic images and observed promising results. Additionally, an indepth analysis of the stop criterion used in the SE estimation algorithm is provided leading to the conclusion that a fixed, user-defined threshold is generally not feasible. Thus, we present new ideas how to develop a non-parametric version of the SE estimation algorithm using entropy.
Reduktion von Rissartefakten durch nicht-lineare Registrierung in histologischen Schnittbildern
(2009)
In dieser Arbeit wird ein Verfahren vorgestellt, das Rissartefakte, die in histologischen Rattenhirnschnitten vorkommen können, durch nicht-lineare Registrierung reduziert. Um die Optimierung in der Rissregion zu leiten, wird der Curvature Registrierungsansatz um eine Metrik basierend auf der Segmentierung der Bilder erweitert. Dabei erzielten Registrierungen mit der ausschließlichen Segmentierung des Risses bessere Ergebnisse als Registrierungen mit einer Segmentierung des gesamten Hirnschnitts. Insgesamt zeigt sich eine deutliche Verbesserung in der Rissregion, wobei der verbleibende reduzierte Riss auf die Glattheitsbedingungen des Regularisierers zurückzuführen ist.
Based on previous work by our group with manual annotation of visible Barrett oesophagus (BE) cancer images, a real-time deep learning artificial intelligence (AI) system was developed. While an expert endoscopist conducts the endoscopic assessment of BE, our AI system captures random images from the real-time camera livestream and provides a global prediction (classification), as well as a dense prediction (segmentation) differentiating accurately between normal BE and early oesophageal adenocarcinoma (EAC). The AI system showed an accuracy of 89.9% on 14 cases with neoplastic BE.
Introduction
We present a clinical case showing the real-time detection, characterization and delineation of an early Barrett’s cancer using AI.
Patients and methods
A 70-year old patient with a long-segment Barrett’s esophagus (C5M7) was assessed with an AI algorithm.
Results
The AI system detected a 10 mm focal lesion and AI characterization predicted cancer with a probability of >90%. After ESD resection, histopathology showed mucosal adenocarcinoma (T1a (m), R0) confirming AI diagnosis.
Conclusion
We demonstrate the real-time AI detection, characterization and delineation of a small and early mucosal Barrett’s cancer.
Aims
AI has proven great potential in assisting endoscopists in diagnostics, however its role in therapeutic endoscopy remains unclear. Endoscopic submucosal dissection (ESD) is a technically demanding intervention with a slow learning curve and relevant risks like bleeding and perforation. Therefore, we aimed to develop an algorithm for the real-time detection and delineation of relevant structures during third-space endoscopy.
Methods
5470 still images from 59 full length videos (47 ESD, 12 POEM) were annotated. 179681 additional unlabeled images were added to the training dataset. Consequently, a DeepLabv3+ neural network architecture was trained with the ECMT semi-supervised algorithm (under review elsewhere). Evaluation of vessel detection was performed on a dataset of 101 standardized video clips from 15 separate third-space endoscopy videos with 200 predefined blood vessels.
Results
Internal validation yielded an overall mean Dice score of 85% (68% for blood vessels, 86% for submucosal layer, 88% for muscle layer). On the video test data, the overall vessel detection rate (VDR) was 94% (96% for ESD, 74% for POEM). The median overall vessel detection time (VDT) was 0.32 sec (0.3 sec for ESD, 0.62 sec for POEM).
Conclusions
Evaluation of the developed algorithm on a video test dataset showed high VDR and quick VDT, especially for ESD. Further research will focus on a possible clinical benefit of the AI application for VDR and VDT during third-space endoscopy.
Quantitative Farbmessungen sollen die Diagnostik laryngealer Erkrankungen unterstützen. Dabei wird der Farbeindruck nicht nur durch die Reflexionseigenschaften des Gewebes sondern auch durch die Farbe der verwendeten Lichtquelle beeinflußt. Der hier vorgestellte Farbkonstanz-Algorithmus basiert auf dem dichromatischen Reflexionsmodell und liefert eine pixelweise Trennung des Farbbildes in seine beiden Faxbanteile. Die Körperfarbe entspricht dabei der gewebespezifischen Reflexion, die Oberfächenfarbe der Strahlung der Lichtquelle.
Quantification of growth in experimental F98 and C6 rat brain tumours was performed on 51 rat brains, 17 of which have been further assessed by 3D tumour reconstruction. Brains were cryosliced and radio-labelled with a ligand of the peripheral type benzodiazepine-receptor (pBR), 3H-Pk11195 [(1-(2-chlorophenyl)-N-methyl-N-(1-methyl-propylene)-3-isoquinoline-carboxamide)] by receptor autoradiography. Manually segmented and automatically registered tumours have been 3D-reconstructed for volumetric comparison on the basis of 3H-Pk11195-based tumour recognition. Furthermore automatically computed areas of −300 μm inner (marginal) zone as well as 300 μm and 600 μm outer tumour space were quantified. These three different regions were transferred onto other adjacent slices that had been labelled by receptor autoradiography with the A1 Adenosine receptor (A1AR)-ligand 3H-CPFPX (3H-8-cyclopentyl-3-(3-fluorpropyl)-1-propylxanthine) for quantitative assessment of A1AR in the three different tumour zones. Hence, a method is described for quantifying various receptor protein systems in the tumour as well as in the marginal invasive zones around experimentally implanted rat brain tumours and their representation in the tumour microenvironment as well as in 3D space. Furthermore, a tool for automatically reading out radio-labelled rat brain slices from auto radiographic films was developed, reconstructed into a consistent 3D-tumour model and the zones around the tumour were visualized. A1AR expression was found to depend upon the tumour volume in C6 animals, but is independent on the time of tumour development. In F98 animals, a significant increase in A1AR receptor protein was found in the Peritumoural zone as a function of time of tumour development and tumour volume.
Although it has long been presumed that d-amino acids are uncommon in mammalians, substantial amounts of free d-serine have been detected in the mammalian brain. d-Serine has been demonstrated to be an important modulator of glutamatergic neurotransmission and acts as an agonist at the strychnine-insensitive glycine site of N-methyl-d-aspartate receptors. The blood-to-brain transfer of d-serine is thought to be extremely low, and it is assumed that d-serine is generated by isomerization of l-serine in the brain. Stimulated by the observation of a preferred transport of the d-isomer of proline at the blood–brain barrier, we investigated the differential uptake of [3H]-d-serine and [3H]-l-serine in the rat brain 1 h after intravenous injection using quantitative autoradiography. Surprisingly, brain uptake of [3H]-d-serine was significantly higher than that of [3H]-l-serine, indicating a preferred transport of the d-enantiomer of serine at the blood–brain barrier. This finding indicates that exogenous d-serine may have a direct influence on glutamatergic neurotransmission and associated diseases.
Digital nerve lesions result in a loss of tactile sensation reflected by an anesthetic area (AA) at the radial or ulnar aspect of the respective digit. Yet, available tools to monitor the recovery of tactile sense have been criticized for their lack of validity. However, the precise quantification of AA dynamics by three-dimensional (3-D) imaging could serve as an accurate surrogate to monitor recovery following digital nerve repair.
For validation, AAs were marked on digits of healthy volunteers to simulate the AA of an impaired cutaneous innervation. Three dimensional models were composed from raw images that had been acquired with a 3-D camera (Vectra H2) to precisely quantify relative AA for each digit (3-D models, n= 80). Operator properties varied regarding individual experience in 3-D imaging and image processing. Additionally, the concept was applied in a clinical case study.
Images taken by experienced photographers were rated better quality (p< 0.001) and needed less processing time (p= 0.020). Quantification of the relative AA was neither altered significantly by experience levels of the photographer (p= 0.425) nor the image assembler (p= 0.749).
The proposed concept allows precise and reliable surface quantification of digits and can be performed consistently without relevant distortion by lack of examiner experience. Routine 3-D imaging of the AA has the great potential to provide visual evidence of various returning states of sensation and to convert sensory nerve recovery into a metric variable with high responsiveness to temporal progress.
Aims
VA is an endoscopic finding of celiac disease (CD), which can easily be missed if pretest probability is low. In this study, we aimed to develop an artificial intelligence (AI) algorithm for the detection of villous atrophy on endoscopic images.
Methods
858 images from 182 patients with VA and 846 images from 323 patients with normal duodenal mucosa were used for training and internal validation of an AI algorithm (ResNet18). A separate dataset was used for external validation, as well as determination of detection performance of experts, trainees and trainees with AI support. According to the AI consultation distribution, images were stratified into “easy” and “difficult”.
Results
Internal validation showed 82%, 85% and 84% for sensitivity, specificity and accuracy. External validation showed 90%, 76% and 84%. The algorithm was significantly more sensitive and accurate than trainees, trainees with AI support and experts in endoscopy. AI support in trainees was associated with significantly improved performance. While all endoscopists showed significantly lower detection for “difficult” images, AI performance remained stable.
Conclusions
The algorithm outperformed trainees and experts in sensitivity and accuracy for VA detection. The significant improvement with AI support suggests a potential clinical benefit. Stable performance of the algorithm in “easy” and “difficult” test images may indicate an advantage in macroscopically challenging cases.
Background: For surgical fixation of bone fractures of the human hand, so-called Kirschner-wires (K-wires) are drilled through bone fragments. Due to the minimally invasive drilling procedures without a view of risk structures like vessels and nerves, a thorough training of young surgeons is necessary. For the development of a virtual reality (VR) based training system, a three-dimensional (3D) printed phantom hand is required. To ensure an intuitive operation, this phantom hand has to be realistic in both, its position relative to the driller as well as in its haptic features. The softest 3D printing material available on the market, however, is too hard to imitate human soft tissue. Therefore, a support-material (SUP) filled metamaterial is used to soften the raw material. Realistic haptic features are important to palpate protrusions of the bone to determine the drilling starting point and angle. An optical real-time tracking is used to transfer position and rotation to the training system.
Methods: A metamaterial already developed in previous work is further improved by use of a new unit cell. Thus, the amount of SUP within the volume can be increased and the tissue is softened further. In addition, the human anatomy is transferred to the entire hand model. A subcutaneous fat layer and penetration of air through pores into the volume simulate shiftability of skin layers. For optical tracking, a rotationally symmetrical marker attached to the phantom hand with corresponding reference marker is developed. In order to ensure trouble-free position transmission, various types of marker point applications are tested.
Results: Several cuboid and forearm sample prints lead to a final 30 centimeter long hand model. The whole haptic phantom could be printed faultless within about 17 hours. The metamaterial consisting of the new unit cell results in an increased SUP share of 4.32%. Validated by an expert surgeon study, this allows in combination with a displacement of the uppermost skin layer a good palpability of the bones. Tracking of the hand marker in dodecahedron design works trouble-free in conjunction with a reference marker attached to the worktop of the training system.
Conclusions: In this work, an optically tracked and haptically correct phantom hand was developed using dual-material 3D printing, which can be easily integrated into a surgical training system.
Einleitung
Übermäßige Bewegung im Bild kann die Performance von auf künstlicher Intelligenz (KI) basierenden klinischen Entscheidungsunterstützungssystemen (CDSS) reduzieren. Optical Flow (OF) ist eine Methode zur Lokalisierung und Quantifizierung von Bewegungen zwischen aufeinanderfolgenden Bildern.
Ziel
Ziel ist es, die Mensch-Computer-Interaktion (HCI) zu verbessern und Endoskopiker die unser KI-System „Barrett-Ampel“ zur Unterstützung bei der Beurteilung von Barrett-Ösophagus (BE) verwenden, ein Echtzeit-Feedback zur aktuellen Datenqualität anzubieten.
Methodik
Dazu wurden unveränderte Videos in „Weißlicht“ (WL), „Narrow Band Imaging“ (NBI) und „Texture and Color Enhancement Imaging“ (TXI) von acht endoskopischen Untersuchungen von histologisch gesichertem BE und mit Barrett-Ösophagus assoziierten Neoplasien (BERN) durch unseren KI-Algorithmus analysiert. Der zur Bewertung der Bildqualität verwendete OF beinhaltete die mittlere Magnitude und die Entropie des Histogramms der Winkel. Frames wurden automatisch extrahiert, wenn die vordefinierten Schwellenwerte von 3,0 für die mittlere Magnitude und 9,0 für die Entropie des Histogramms der Winkel überschritten wurden. Experten sahen sich zunächst die Videos ohne KI-Unterstützung an und bewerteten, ob Störfaktoren die Sicherheit mit der eine Diagnose im vorliegenden Fall gestellt werden kann negativ beeinflussen. Anschließend überprüften sie die extrahierten Frames.
Ergebnis
Gleichmäßige Bewegung in eine Richtung, wie etwa beim Vorschieben des Endoskops, spiegelte sich, bei insignifikant veränderter Entropie, in einer Erhöhung der Magnitude wider. Chaotische Bewegung, zum Beispiel während dem Spülen, war mit erhöhter Entropie assoziiert. Insgesamt war eine unruhige endoskopische Darstellung, Flüssigkeit sowie übermäßige Ösophagusmotilität mit erhöhtem OF assoziiert und korrelierte mit der Meinung der Experten über die Qualität der Videos. Der OF und die subjektive Wahrnehmung der Experten über die Verwertbarkeit der vorliegenden Bildsequenzen korrelierten direkt proportional. Wenn die vordefinierten Schwellenwerte des OF überschritten wurden, war die damit verbundene Bildqualität in 94% der Fälle für eine definitive Interpretation auch für Experten unzureichend.
Schlussfolgerung
OF hat das Potenzial Endoskopiker ein Echtzeit-Feedback über die Qualität des Dateninputs zu bieten und so nicht nur die HCI zu verbessern, sondern auch die optimale Performance von KI-Algorithmen zu ermöglichen.
Video Documentation of endoscopic findings simplifies diagnostic counseling of the patient and aids pre-operative discussion among the medical team. Judgment of such images is still subjective and can not give a quantitative evaluation of the disease process regarding diagnosis or response to treatment. Modern treatment of early laryngeal cancer with laserablation requires intensive follow up and frequent direct laryngoscopy under general anesthesia with blind biopsies to detect any tumor residual or recurrence. Inflammatory conditions of the larynx are frequently confused with other causes of dysphonia. Mapping anddigital analysis of the documented image will suggest the tumor site and avoids undue blind biopsies under anesthesia. However, varying illumination results in different colors reflected from the same object. To achieve quantitative analysis, color constancy has to be assured. Inthis paper, the environment is presented which allow the objective judgment of larngoscopies.
Nutzung eines Farbkonstanz-Algorithmus zur Entfernung von Glanzlichtern in laryngoskopischen Bildern
(1998)
1 Einführung
Funktionelle und organische Störungen im Larynx beeinträchtigen die Ausdrucksfähigkeit des Menschen. Zur Diagnostik und Verlaufkontrolle werden die Stimmlippen im Larynx mit Hilfe der Video-Laryngoskopie aufgenommen. Zur optimalen Farbmessung wird dazu an das Lupenendoskop eine 3-Chip-CCD-Kamera angeschlossen, die eine unabhängige Aufnahme der drei Farbkanäle erlaubt. Die bisherige subjektive Befundung ist von der Erfahrung des Untersuchers abhängig und läßt nur eine grobe Klassifikation der Krankheitsbilder zu. Zur Objektivierung werden daher quantitative Parameter für Farbe, Textur und Schwingung entwickelt. Neben dem Einfluß der wechselnden Lichtquellenfarbe auf den Farbeindruck ist die Sekretauflage auf den Stimmlippen ein Problem bei der Farb-und Texturanalyse. Sie kann zu ausgedehnten Glanzlichtern führen und so weite Bereiche der Stimmlippen für die Farb-und Texturanalyse unbrauchbar machen. Dieser Beitrag stellt einen Farbkonstanz-Algorithmus vor, der unabhängig von der Lichtquelle quantitative Farbwerte des Gewebes liefert und die Glanzlichtdetektion und -elimination ermöglicht.
2 Methodik
Ziel des Farbkonstanz-Algorithmus ist die Trennung von Lichtquellen-und Gewebefarbe. Unter Verwendung des dichromatischen Reflexionsmodells [1] kann die Oberflächenreflexion mit der Farbe der Lichtquelle und die Körperreflexion mit der Gewebefarbe identifiziert werden. Der Farbeindruck entsteht aus der Linearkombination beider Farbkomponenten. Ihre Gewichtung ist von der Aufnahmegeometrie abhängig, insbesondere vom Winkel zwischen Oberflächennormalen und dem Positionsvektor der Lichtquelle. In einem zweistufigen Verfahren wird zunächst die Lichtquellenfarbe geschätzt, dann die Gewebefarbe ermittelt. Hieraus können beide Farbanteile durch die Berechnung der Gewichtsfaktoren pixelweise getrennt werden.
Signal transmission between different brain regions requires connecting fiber tracts, the structural basis of the human connectome. In contrast to animal brains, where a multitude of tract tracing methods can be used, magnetic resonance (MR)-based diffusion imaging is presently the only promising approach to study fiber tracts between specific human brain regions. However, this procedure has various inherent restrictions caused by its relatively low spatial resolution. Here, we introduce 3D-polarized light imaging (3D-PLI) to map the three-dimensional course of fiber tracts in the human brain with a resolution at a submillimeter scale based on a voxel size of 100 μm isotropic or less. 3D-PLI demonstrates nerve fibers by utilizing their intrinsic birefringence of myelin sheaths surrounding axons. This optical method enables the demonstration of 3D fiber orientations in serial microtome sections of entire human brains. Examples for the feasibility of this novel approach are given here. 3D-PLI enables the study of brain regions of intense fiber crossing in unprecedented detail, and provides an independent evaluation of fiber tracts derived from diffusion imaging data.
Purpose: In this trial, we used a previously developed prototype software to assess aesthetic results after reconstructive surgery for congenital breast asymmetry using automated anthropometry. To prove the consensus between the manual and automatic digital measurements, we evaluated the software by comparing the manual and automatic measurements of 46 breasts.
Methods: Twenty-three patients who underwent reconstructive surgery for congenital breast asymmetry at our institution were examined and underwent 3D surface imaging. Per patient, 14 manual and 14 computer-based anthropometric measurements were obtained according to a standardized protocol. Manual and automatic measurements, as well as the previously proposed Symmetry Index (SI), were compared.
Results: The Wilcoxon signed-rank test revealed no significant differences in six of the seven measurements between the automatic and manual assessments. The SI showed robust agreement between the automatic and manual methods.
Conclusion: The present trial validates our method for digital anthropometry. Despite the discrepancy in one measurement, all remaining measurements, including the SI, showed high agreement between the manual and automatic methods. The proposed data bring us one step closer to the long-term goal of establishing robust instruments to evaluate the results of breast surgery.
The early diagnosis of cancer in Barrett’s esophagus is crucial for improving the prognosis. However, identifying Barrett’s esophagus-related neoplasia (BERN) is challenging, even for experts [1]. Four-quadrant biopsies may improve the detection of neoplasia, but they can be associated with sampling errors. The application of artificial intelligence (AI) to the assessment of Barrett’s esophagus could improve the diagnosis of BERN, and this has been demonstrated in both preclinical and clinical studies [2] [3].
In this video demonstration, we show the accurate detection and delineation of BERN in two patients ([Video 1]). In part 1, the AI system detects a mucosal cancer about 20 mm in size and accurately delineates the lesion in both white-light and narrow-band imaging. In part 2, a small island of BERN with high-grade dysplasia is detected and delineated in white-light, narrow-band, and texture and color enhancement imaging. The video shows the results using a transparent overlay of the mucosal cancer in real time as well as a full segmentation preview. Additionally, the optical flow allows for the assessment of endoscope movement, something which is inversely related to the reliability of the AI prediction. We demonstrate that multimodal imaging can be applied to the AI-assisted detection and segmentation of even small focal lesions in real time.
Purpose
MR-based attenuation correction (AC) will become an integral part of combined PET/MR systems. Here, we propose a toolbox to validate MR-AC of clinical PET/MRI data sets.
Methods
Torso scans of ten patients were acquired on a combined PET/CT and on a 1.5-T MRI system. MR-based attenuation data were derived from the CT following MR–CT image co-registration and subsequent histogram matching. PET images were reconstructed after CT- (PET/CT) and MR-based AC (PET/MRI). Lesion-to-background (L/B) ratios were estimated on PET/CT and PET/MRI.
Results
MR–CT histogram matching leads to a mean voxel intensity difference in the CT- and MR-based attenuation images of 12% (max). Mean differences between PET/MRI and PET/CT were 19% (max). L/B ratios were similar except for the lung where local misregistration and intensity transformation leads to a biased PET/MRI.
Conclusion
Our toolbox can be used to study pitfalls in MR-AC. We found that co-registration accuracy and pixel value transformation determine the accuracy of PET/MRI.
Real-time computational speed and a high degree of precision are requirements for computer-assisted interventions. Applying a segmentation network to a medical video processing task can introduce significant inter-frame prediction noise. Existing approaches can reduce inconsistencies by including temporal information but often impose requirements on the architecture or dataset. This paper proposes a method to include temporal information in any segmentation model and, thus, a technique to improve video segmentation performance without alterations during training or additional labeling. With Motion-Corrected Moving Average, we refine the exponential moving average between the current and previous predictions. Using optical flow to estimate the movement between consecutive frames, we can shift the prior term in the moving-average calculation to align with the geometry of the current frame. The optical flow calculation does not require the output of the model and can therefore be performed in parallel, leading to no significant runtime penalty for our approach. We evaluate our approach on two publicly available segmentation datasets and two proprietary endoscopic datasets and show improvements over a baseline approach.
In the field of computer- and robot-assisted minimally invasive surgery, enormous progress has been made in recent years based on the recognition of surgical instruments in endoscopic images. Especially the determination of the position and type of the instruments is of great interest here. Current work involves both spatial and temporal information with the idea, that the prediction of movement of surgical tools over time may improve the quality of final segmentations. The provision of publicly available datasets has recently encouraged the development of new methods, mainly based on deep learning. In this review, we identify datasets used for method development and evaluation, as well as quantify their frequency of use in the literature. We further present an overview of the current state of research regarding the segmentation and tracking of minimally invasive surgical instruments in endoscopic images. The paper focuses on methods that work purely visually without attached markers of any kind on the instruments, taking into account both single-frame segmentation approaches as well as those involving temporal information. A discussion of the reviewed literature is provided, highlighting existing shortcomings and emphasizing available potential for future developments. The publications considered were identified through the platforms Google Scholar, Web of Science, and PubMed. The search terms used were "instrument segmentation", "instrument tracking", "surgical tool segmentation", and "surgical tool tracking" and result in 408 articles published between 2015 and 2022 from which 109 were included using systematic selection criteria.
In the field of computer- and robot-assisted minimally invasive surgery, enormous progress has been made in recent years based on the recognition of surgical instruments in endoscopic images and videos. In particular, the determination of the position and type of instruments is of great interest. Current work involves both spatial and temporal information, with the idea that predicting the movement of surgical tools over time may improve the quality of the final segmentations. The provision of publicly available datasets has recently encouraged the development of new methods, mainly based on deep learning. In this review, we identify and characterize datasets used for method development and evaluation and quantify their frequency of use in the literature. We further present an overview of the current state of research regarding the segmentation and tracking of minimally invasive surgical instruments in endoscopic images and videos. The paper focuses on methods that work purely visually, without markers of any kind attached to the instruments, considering both single-frame semantic and instance segmentation approaches, as well as those that incorporate temporal information. The publications analyzed were identified through the platforms Google Scholar, Web of Science, and PubMed. The search terms used were “instrument segmentation”, “instrument tracking”, “surgical tool segmentation”, and “surgical tool tracking”, resulting in a total of 741 articles published between 01/2015 and 07/2023, of which 123 were included using systematic selection criteria. A discussion of the reviewed literature is provided, highlighting existing shortcomings and emphasizing the available potential for future developments.
Mass spectrometric imaging (MSI) is a young innovative analytical technique and combines different fields of advanced mass spectrometry and biomedical research with the aim to provide maps of elements and molecules, complexes or fragments. Especially essential metals such as zinc, copper, iron and manganese play a functional role in signaling, metabolism and homeostasis of the cell. Due to the high degree of spatial organization of metals in biological systems their distribution analysis is of key interest in life sciences. We have developed analytical techniques termed BrainMet using laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) imaging to measure the distribution of trace metals in biological tissues for biomedical research and feasibility studies—including bioaccumulation and bioavailability studies, ecological risk assessment and toxicity studies in humans and other organisms. The analytical BrainMet techniques provide quantitative images of metal distributions in brain tissue slices which can be combined with other imaging modalities such as photomicrography of native or processed tissue (histochemistry, immunostaining) and autoradiography or with in vivo techniques such as positron emission tomography or magnetic resonance tomography.
Prospective and instrumental developments will be discussed concerning the development of the metalloprotein microscopy using a laser microdissection (LMD) apparatus for specific sample introduction into an inductively coupled plasma mass spectrometer (LMD-ICP-MS) or an application of the near field effect in LA-ICP-MS (NF-LA-ICP-MS). These nano-scale mass spectrometric techniques provide improved spatial resolution down to the single cell level.
In dieser Arbeit wird die Segmentierung von Gehirngewebe aus Kopfaufnahmen von Ratten mittels Level-Set-Methoden vorgeschlagen. Dazu wird ein zweidimensionaler, kontrastbasierter Ansatz zu einem dreidimensionalen, lokal an die Bildintensität adaptierten Segmentierer erweitert. Es wird gezeigt, dass mit diesem echten 3D-Ansatz die lokalen Bildstrukturen besser berücksichtigt werden können. Insbesondere Magnet-Resonanz-Tomographien (MRTs) mit globalen Helligkeitsgradienten, beispielsweise bedingt durch Oberflächenspulen, können auf diese Weise zuverlässiger und ohne weitere Vorverarbeitungsschritte segmentiert werden. Die Leistungsfähigkeit des Algorithmus wird experimentell an Hand dreier Rattenhirn-MRTs demonstriert.
We present the Regensburg Breast Shape Model (RBSM) – a 3D statistical shape model of the female breast built from 110 breast scans, and the first ever publicly available. Together with the model, a fully automated, pairwise surface registration pipeline used to establish correspondence among 3D breast scans is introduced. Our method is computationally efficient and requires only four landmarks to guide the registration process. In order to weaken the strong coupling between breast and thorax, we propose to minimize the variance outside the breast region as much as possible. To achieve this goal, a novel concept called breast probability masks (BPMs) is introduced. A BPM assigns probabilities to each point of a 3D breast scan, telling how likely it is that a particular point belongs to the breast area. During registration, we use BPMs to align the template to the target as accurately as possible inside the breast region and only roughly outside. This simple yet effective strategy significantly reduces the unwanted variance outside the breast region, leading to better statistical shape models in which breast shapes are quite well decoupled from the thorax. The RBSM is thus able to produce a variety of different breast shapes as independently as possible from the shape of the thorax. Our systematic experimental evaluation reveals a generalization ability of 0.17 mm and a specificity of 2.8 mm for the RBSM. Ultimately, our model is seen as a first step towards combining physically motivated deformable models of the breast and statistical approaches in order to enable more realistic surgical outcome simulation.
We present the Regensburg Breast Shape Model (RBSM)—a 3D statistical shape model of the female breast built from 110 breast scans acquired in a standing position, and the first publicly available. Together with the model, a fully automated, pairwise surface registration pipeline used to establish dense correspondence among 3D breast scans is introduced. Our method is computationally efficient and requires only four landmarks to guide the registration process. A major challenge when modeling female breasts from surface-only 3D breast scans is the non-separability of breast and thorax. In order to weaken the strong coupling between breast and surrounding areas, we propose to minimize the variance outside the breast region as much as possible. To achieve this goal, a novel concept called breast probability masks (BPMs) is introduced. A BPM assigns probabilities to each point of a 3D breast scan, telling how likely it is that a particular point belongs to the breast area. During registration, we use BPMs to align the template to the target as accurately as possible inside the breast region and only roughly outside. This simple yet effective strategy significantly reduces the unwanted variance outside the breast region, leading to better statistical shape models in which breast shapes are quite well decoupled from the thorax. The RBSM is thus able to produce a variety of different breast shapes as independently as possible from the shape of the thorax. Our systematic experimental evaluation reveals a generalization ability of 0.17 mm and a specificity of 2.8 mm. To underline the expressiveness of the proposed model, we finally demonstrate in two showcase applications how the RBSM can be used for surgical outcome simulation and the prediction of a missing breast from the remaining one. Our model is available at https://www.rbsm.re-mic.de/.
Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency–Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.
Presenting images from different modalities seems to be a trivial task considering the challenges to obtain registered images as a pre-requisite for image fusion. In combined tomographs like PET/CT, image registration is intrinsic. However, informative image fusion mandates careful preparation owing to the large amount of information that is presented to the observer. In complex imaging situations it is required to provide tools that are easy to handle and still powerful enough to help the observer discriminating important details from background patterns. We investigated several options for color tables applied to brain and non-brain images obtained with PET, MRI and CT.
Einleitung
Third-Space Interventionen wie die endoskopische Submukosadissektion (ESD) und die perorale endoskopische Myotomie (POEM) sind technisch anspruchsvoll und mit einem erhöhten Risiko für intraprozedurale Komplikationen wie Blutung oder Perforation assoziiert. Moderne Computerprogramme zur Unterstützung bei diagnostischen Entscheidungen werden unter Einsatz von künstlicher Intelligenz (KI) in der Endoskopie bereits erfolgreich eingesetzt. Ziel der vorliegenden Arbeit war es, relevante anatomische Strukturen mithilfe eines Deep-Learning Algorithmus zu detektieren und segmentieren, um die Sicherheit und Anwendbarkeit von ESD und POEM zu erhöhen.
Methoden
Zwölf Videoaufnahmen in voller Länge von Third-Space Endoskopien wurden aus der Datenbank des Universitätsklinikums Augsburg extrahiert. 1686 Einzelbilder wurden für die Kategorien Submukosa, Blutgefäß, Dissektionsmesser und endoskopisches Instrument annotiert und segmentiert. Mit diesem Datensatz wurde ein DeepLabv3+neuronales Netzwerk auf der Basis eines ResNet mit 101 Schichten trainiert und intern anhand der Parameter Intersection over Union (IoU), Dice Score und Pixel Accuracy validiert. Die Fähigkeit des Algorithmus zur Gefäßdetektion wurde anhand von 24 Videoclips mit einer Spieldauer von 7 bis 46 Sekunden mit 33 vordefinierten Gefäßen evaluiert. Anhand dieses Tests wurde auch die Gefäßdetektionsrate eines Experten in der Third-Space Endoskopie ermittelt.
Ergebnisse
Der Algorithmus zeigte eine Gefäßdetektionsrate von 93,94% mit einer mittleren Rate an falsch positiven Signalen von 1,87 pro Minute. Die Gefäßdetektionsrate des Experten lag bei 90,1% ohne falsch positive Ergebnisse. In der internen Validierung an Einzelbildern wurde eine IoU von 63,47%, ein mittlerer Dice Score von 76,18% und eine Pixel Accuracy von 86,61% ermittelt.
Zusammenfassung
Dies ist der erste KI-Algorithmus, der für den Einsatz in der therapeutischen Endoskopie entwickelt wurde. Präliminäre Ergebnisse deuten auf eine mit Experten vergleichbare Detektion von Gefäßen während der Untersuchung hin. Weitere Untersuchungen sind nötig, um die Leistung des Algorithmus im Vergleich zum Experten genauer zu eruieren sowie einen möglichen klinischen Nutzen zu ermitteln.
Introduction – Diagnosis of abnormal cilia function is based on ultrastructural analysis of axoneme defects, especialy the features of inner and outer dynein arms which are the motors of ciliar motility. Sub-optimal biopsy material, methodical, and intrinsic electron microscopy factors pose difficulty in ciliary defects evaluation. We present a computer-assisted approach based on state-of-the-art image analysis and object recognition methods yielding a time-saving and efficient diagnosis of cilia dysfunction. Method – The presented approach is based on a pipeline of basal image processing methods like smoothing, thresholding and ellipse fitting. However, integration of application specific knowledge results in robust segmentations even in cases of image artifacts. The method is build hierarchically starting with the detection of cilia within the image, followed by the detection of nine doublets within each analyzable cilium, and ending with the detection of dynein arms of each doublet. The process is concluded by a rough classification of the dynein arms as basis for a computer-assisted diagnosis. Additionally, the interaction possibilities are designed in a way, that the results are still reproducible given the completion report. Results – A qualitative evaluation showed reasonable detection results for cilia, doublets and dynein arms. However, since a ground truth is missing, the variation of the computer-assisted diagnosis should be within the subjective bias of human diagnosticians. The results of a first quantitative evaluation with five human experts and six images with 12 analyzable cilia showed, that with default parameterization 91.6% of the cilia and 98% of the doublets were found. The computer-assisted approach rated 66% of those inner and outer dynein arms correct, where all human experts agree. However, especially the quality of the dynein arm classification may be improved in future work.
Aims
Barrett´s esophagus related neoplasia (BERN) is difficult to detect and characterize during endoscopy, even for expert endoscopists. We aimed to assess the add-on effect of an Artificial Intelligence (AI) algorithm (Barrett-Ampel) as a decision support system (DSS) for non-expert endoscopists in the evaluation of Barrett’s esophagus (BE) and BERN.
Methods
Twelve videos with multimodal imaging white light (WL), narrow-band imaging (NBI), texture and color enhanced imaging (TXI) of histologically confirmed BE and BERN were assessed by expert and non-expert endoscopists. For each video, endoscopists were asked to identify the area of BERN and decide on the biopsy spot. Videos were assessed by the AI algorithm and regions of BERN were highlighted in real-time by a transparent overlay. Finally, endoscopists were shown the AI videos and asked to either confirm or change their initial decision based on the AI support.
Results
Barrett-Ampel correctly identified all areas of BERN, irrespective of the imaging modality (WL, NBI, TXI), but misinterpreted two inflammatory lesions (Accuracy=75%). Expert endoscopists had a similar performance (Accuracy=70,8%), while non-experts had an accuracy of 58.3%. When AI was implemented as a DSS, non-expert endoscopists improved their diagnostic accuracy to 75%.
Conclusions
AI may have the potential to support non-expert endoscopists in the assessment of videos of BE and BERN. Limitations of this study include the low number of videos used. Randomized clinical trials in a real-life setting should be performed to confirm these results.
Background: Currently, it is common practice to use three-dimensional (3D) printers not only for rapid prototyping in the industry, but also in the medical area to create medical applications for training inexperienced surgeons. In a clinical training simulator for minimally invasive bone drilling to fix hand fractures with Kirschner-wires (K-wires), a 3D-printed hand phantom must not only be geometrically but also haptically correct. Due to a limited view during an operation, surgeons need to perfectly localize underlying risk structures only by feeling of specific bony protrusions of the human hand.
Methods: The goal of this experiment is to imitate human soft tissue with its haptic and elasticity for a realistic hand phantom fabrication, using only a dual-material 3D printer and support-material-filled metamaterial between skin and bone. We present our workflow to generate lattice structures between hard bone and soft skin with iterative cube edge (CE) or cube face (CF) unit cells. Cuboid and finger shaped sample prints with and without inner hard bone in different lattice thickness are constructed and 3D printed.
Results: The most elastic available rubber-like material is too firm to imitate soft tissue. By reducing the amount of rubber in the inner volume through support material (SUP), objects become significantly softer. Without metamaterial, after disintegration, the SUP can be shifted through the volume and thus the body loses its original shape. Although the CE design increases the elasticity, it cannot restore the fabric form. In contrast to CE, the CF design increases not only the elasticity but also guarantees a local limitation of the SUP. Therefore, the body retains its shape and internal bones remain in its intended place. Various unit cell sizes, lattice thickening and skin thickness regulate the rubber material and SUP ratio. Test prints with higher SUP and lower rubber material percentage appear softer and vice versa. This was confirmed by an expert surgeon evaluation. Subjects adjudged pure rubber-like material as too firm and samples only filled with SUP or lattice structure in CE design as not suitable for imitating tissue. 3D-printed finger samples in CF design were rated as realistic compared to the haptic of human tissue with a good palpable bone structure.
Conclusions: We developed a new dual-material 3D print technique to imitate soft tissue of the human hand with its haptic properties. Blowy SUP is trapped within a lattice structure to soften rubber-like 3D print material, which makes it possible to reproduce a realistic replica of human hand soft tissue.
Metals are involved in many processes of life. They are needed for enzymatic reactions, are involved in healthy processes but also yield diseases if the metal homeostasis is disordered. Therefore, the interest to assess the spatial distribution of metals is rising in biomedical science. Imaging metal (and non-metal) isotopes by laser ablation mass spectrometry with inductively coupled plasma (LA-ICP-MS) requires a special software solution to process raw data obtained by scanning a sample line-by-line. As no software ready to use was available we developed an interactive software tool for Image Generation and Analysis (IMAGENA). Unless optimised for LA-ICP-MS, IMAGENA can handle other raw data as well. The general purpose was to reconstruct images from a continuous list of raw data points, to visualise these images, and to convert them into a commonly readable image file format that can be further analysed by standard image analysis software. The generation of the image starts with loading a text file that holds a data column of every measured isotope. Specifying general spatial domain settings like the data offset and the image dimensions is done by the user getting a direct feedback by means of a preview image. IMAGENA provides tools for calibration and to correct for a signal drift in the y-direction. Images are visualised in greyscale as well a pseudo-colours with possibilities for contrast enhancement. Image analysis is performed in terms of smoothed line plots in row and column direction.
The German workshop on medical image computing (BVM) has been held in different locations in Germany for more than 20 years. In terms of content, BVM focused on the computer-aided analysis of medical image data with a wide range of applications, e.g. in the area of imaging, diagnostics, operation planning, computer-aided intervention and visualization.
During this time, there have been remarkable methodological developments and upheavals, on which the BVM community has worked intensively. The area of machine learning should be emphasized, which has led to significant improvements, especially for tasks of classification and segmentation, but increasingly also in image formation and registration. As a result, work in connection with deep learning now dominates the BVM. These developments have also contributed to the establishment of medical image processing at the interface between computer science and medicine as one of the key technologies for the digitization of the health system.
In addition to the presentation of current research results, a central aspect of the BVM is primarily the promotion of young scientists from the diverse BVM community, covering not only Germany but also Austria, Switzerland, The Netherland and other European neighbors. The conference serves primarily doctoral students and postdocs, but also students with excellent bachelor and master theses as a platform to present their work, to enter into professional discourse with the community, and to establish networks with specialist colleagues. Despite the many conferences and congresses that are also relevant for medical image processing, the BVM has therefore lost none of its importance and attractiveness and has retained its permanent place in the annual conference rhythm.
Building on this foundation, there are some innovations and changes this year. The BVM 2021 was organized for the first time at the Ostbayerische Technische Hochschule Regensburg (OTH Regensburg, a technical university of applied sciences). After Aachen, Berlin, Erlangen, Freiburg, Hamburg, Heidelberg, Leipzig, Lübeck, and Munich, Regensburg is not just a new venue. OTH Regensburg is the first representative of the universities of applied sciences (HAW) to organize the conference, which differs to universities, university hospitals, or research centers like Fraunhofer or Helmholtz. This also considers the further development of the research landscape in Germany, where HAWs increasingly contribute to applied research in addition to their focus on teaching. This development is also reflected in the contributions submitted to the BVM in recent years.
At BVM 2021, which was held in a virtual format for the first time due to the Corona pandemic, an attractive and high-quality program was offered. Fortunately, the number of submissions increased significantly. Out of 97 submissions, 26 presentations, 51 posters and 5 software demonstrations were accepted via an anonymized reviewing process with three reviews each. The three best works have been awarded BVM prizes, selected by a separate committee.
Based on these high-quality submissions, we are able to present another special issue in the International Journal of Computer Assisted Radiology and Surgery (IJCARS). Out of the 97 submissions, the ones with the highest scores have been invited to submit an extended version of their paper to be presented in IJCARS. As a result, we are now able to present this special issue with seven excellent articles. Many submissions focus on machine learning in a medical context.
The field of AI is characterized by robust promises, astonishing successes, and remarkable breakthroughs. AI will play a major role in all domains of clinical medicine, but the role of AI in relation to the physician is not yet completely determined. The term artificial intelligence or AI is broad, and several different terms are used in this context that must be organized and demystified. This chapter will review the key concepts and methods of AI, and will introduce some of the different roles for AI in relation to the physician.
Zur Lösung komplexer Segmentierungsprobleme wird eine hierarchische und farbbasierte Wasserscheidentransformation vorgestellt. Geringe Modifikationen bezüglich Startpunktwahl und Flutungsprozess resultieren in signifikanten Verbesserungen der Segmentierung. Das Verfahren wurde zur Lippendetektion in Farbbildsequenzen eingesetzt, die zur quantitativen Beschreibung von Sprechbewegungsabläufen automatisch ausgewertet werden. Die Experimente mit 245 Bildern aus 6 Sequenzen zeigten eine Fehlerrate von 13%.
Die Implantation eines Cochlea Implantates benötigt einen chirurgischen Zugang im Felsenbein und durch die Paukenhöhle des Patienten. Der Chirurg hat eine eingeschränkte Sicht im Operationsgebiet, die weiterhin viele Risikostrukturen enthält. Um eine Cochlea Implantation sicher und fehlerfrei durchzuführen, ist eine umfangreiche theoretische und praktische (teilweise berufsbegleitende) Fortbildung sowie langjährige Erfahrung notwendig. Unter Nutzung von realen klinischen CT/MRT Daten von Innen- und Mittelohr und der interaktiven Segmentierung der darin abgebildeten Strukturen (Nerven, Cochlea, Gehörknöchelchen,...) wird im HaptiVisT Projekt ein haptisch-visuelles Trainingssystem für die Implantation von Innen- und Mittelohr-Implantaten realisiert, das als sog. „Serious Game“ mit immersiver Didaktik gestaltet wird. Die Evaluierung des Demonstrators hinsichtlich Zweckmäßigkeit erfolgt prozessbegleitend und ergebnisorientiert, um mögliche technische oder didaktische Fehler vor Fertigstellung des Systems aufzudecken. Drei zeitlich versetzte Evaluationen fokussieren dabei chirurgisch-fachliche, didaktische sowie haptisch-ergonomische Akzeptanzkriterien.
Die Lehre der medizinischen Bildverarbeitung vermittelt Kenntnisse mit einem breiten Methodenspektrum. Neben den Grundlagen der Verfahren soll ein Gefühl für eine geeignete Ausführungsreihenfolge und ihrer Wirkung auf medizinische Bilddaten entwickelt werden. Die Komplexität der Methoden erfordert vertiefte Programmierkenntnisse, sodass bereits einfache Operationen mit großem Programmieraufwand verbunden sind. Die Software GraphMIC stellt Bildverarbeitungsoperationen in Form interaktiver Knoten zur Verfügung und erlaubt das Arrangieren, Parametrisieren und Ausführen komplexer Verarbeitungssequenzen in einem Graphen. Durch den Fokus auf das Design einer Pipeline, weg von sprach- und frameworkspezifischen Implementierungsdetails, lassen sich grundlegende Prinzipien der Bildverarbeitung anschaulich erlernen. In diesem Beitrag stellen wir die visuelle Programmierung mit GraphMIC der nativen Implementierung äquivalenter Funktionen gegenüber. Die in C++ entwickelte Applikation basiert auf Qt, ITK, OpenCV, VTK und MITK.
GraphMIC is a cross-platform image processing application utilizing the libraries ITK and OpenCV. The abstract structure of image processing pipelines is visually represented by user interface components based on modern QtQuick technology and allows users to focus on arrangement and parameterization of operations rather than implementing the equivalent functionality natively in C++. The application's central goal is to improve and simplify the typical workflow by providing various high level features and functions like multi threading, image sequence processing and advanced error handling. A built-in python interpreter allows the creation of custom nodes, where user defined algorithms can be integrated to extend basic functionality. An embedded 2d/3d visual-izer gives feedback of the resulting image of an operation or the whole pipeline. User inputs like seed points, contours or regions are forwarded to the processing pipeline as parameters to offer semi-automatic image computing. We report the main concept of the application and introduce several features and their implementation. Finally, the current state of development as well as future perspectives of GraphMIC are discussed
GinJinn: An object-detection pipeline for automated feature extraction from herbarium specimens
(2020)
PREMISE:
The generation of morphological data in evolutionary, taxonomic, and ecological studies of plants using herbarium material has traditionally been a labor-intensive task. Recent progress in machine learning using deep artificial neural networks (deep learning) for image classification and object detection has facilitated the establishment of a pipeline for the automatic recognition and extraction of relevant structures in images of herbarium specimens.
METHODS AND RESULTS:
We implemented an extendable pipeline based on state-of-the-art deep-learning object-detection methods to collect leaf images from herbarium specimens of two species of the genus Leucanthemum. Using 183 specimens as the training data set, our pipeline extracted one or more intact leaves in 95% of the 61 test images.
CONCLUSIONS:
We establish GinJinn as a deep-learning object-detection tool for the automatic recognition and extraction of individual leaves or other structures from herbarium specimens. Our pipeline offers greater flexibility and a lower entrance barrier than previous image-processing approaches based on hand-crafted features.
Objective:
Complex hue/saturation images as a new approach for color texture classification using Gabor filters are introduced and compared with common techniques.
Method:
The interpretation of hue and saturationas polar coordinates allows direct use of the HSV-colorspace for Fourier transform. This technique is applied for Gabor feature extraction of color textures. In contrast to other color features based on the RGB-colorspace [1] the combination of color bands is done previous to the filtering.
Results:
The performance of the new HS-featuresis compared with that of RGB based as well as grayscale Gabor features by evaluating the classifi-cation of 30 natural textures. The new HS-featuresshow same results like the best RGB features but allow a more compact representation. On the averagethe color features improve the results of grayscale features.
Conclusion:
The consideration of the color information enhances the classification of color texture. The choice of colorspace cannot be adjudged finally, but the introduced features suggest the use of the HSV-colorspace with less features than RGB.
Serial 2D section images with high resolution, resulting from innovative imaging methods become even more valuable, if they are fused with in vivo volumes. Achieving this goal, the 3D context of the sections would be restored, the deformations would be corrected and the artefacts would be eliminated. However, the registration in this field faces big challenges and is not solved in general. On the other hand, several approaches have been introduced dealing at least with some of these difficulties. Here, a brief overview of the topic is given and some of the solutions are presented. It does not constitute the claim to be a complete review, but could be a starting point for those who are interested in this field.
Fusion of histology and MRI is frequently demanded in biomedical research to study in vitro tissue properties in an in vivo reference space. Distortions and artifacts caused by cutting and staining of histological slices as well as differences in spatial resolution make even the rigid fusion a difficult task. State-of- the-art methods start with a mono-modal restacking yielding a histological pseudo-3D volume. The 3D information of the MRI reference is considered subsequently. However, consistency of the histology volume and consistency due to the corresponding MRI seem to be diametral goals. Therefore, we propose a novel fusion framework optimizing histology/histology and histology/MRI consistency at the same time finding a balance between both goals. Method - Direct slice-to-slice correspondence even in irregularly-spaced cutting sequences is achieved by registration-based interpolation of the MRI. Introducing a weighted multi-image mutual information metric (WI), adjacent histology and corresponding MRI are taken into account at the same time. Therefore, the reconstruction of the histological volume as well as the fusion with the MRI is done in a single step. Results - Based on two data sets with more than 110 single registrations in all, the results are evaluated quantitatively based on Tanimoto overlap measures and qualitatively showing the fused volumes. In comparison to other multi-image metrics, the reconstruction based on WI is significantly improved. We evaluated different parameter settings with emphasis on the weighting term steering the balance between intra- and inter-modality consistency.
Forschungsbericht 2017
(2017)
Forschungsbericht 2016
(2016)
Forschungsbericht 2012
(2012)
Forschung 2019
(2019)
In order to fix a fracture using minimally invasive surgery approaches, surgeons are drilling complex and tiny bones with a 2 dimensional X-ray as single imaging modality in the operating room. Our novel haptic force-feedback and visual assisted training system will potentially help hand surgeons to learn the drilling procedure in a realistic visual environment. Within the simulation, the collision detection as well as the interaction between virtual drill, bone voxels and surfaces are important. In this work, the chai3d collision detection and force calculation algorithms are combined with a physics engine to simulate the bone drilling process. The chosen Bullet-Physics-Engine provides a stable simulation of rigid bodies, if the collision model of the drill and the tool holder is generated as a compound shape. Three haptic points are added to the K-wire tip for removing single voxels from the bone. For the drilling process three modes are proposed to emulate the different phases of drilling in restricting the movement of a haptic device.
One common method to fix fractures of the human hand after an accident is an osteosynthesis with Kirschner wires (K-wires) to stabilize the bone fragments. The insertion of K-wires is a delicate minimally invasive surgery, because surgeons operate almost without a sight. Since realistic training methods are time consuming, costly and insufficient, a virtual-reality (VR) based training system for the placement of K-wires was developed. As part of this, the current work deals with the real-time bone drilling simulation using a haptic force-feedback device.
To simulate the drilling, we introduce a virtual fixture based force-feedback drilling approach. By decomposition of the drilling task into individual phases, each phase can be handled individually to perfectly control the drilling procedure. We report about the related finite state machine (FSM), describe the haptic feedback of each state and explain, how to avoid jerking of the haptic force-feedback during state transition.
The usage of the virtual fixture approach results in a good haptic performance and a stable drilling behavior. This was confirmed by 26 expert surgeons, who evaluated the virtual drilling on the simulator and rated it as very realistic. To make the system even more convincing, we determined real drilling feed rates through experimental pig bone drilling and transferred them to our system. Due to a constant simulation thread we can guarantee a precise drilling motion.
Virtual fixtures based force-feedback calculation is able to simulate force-feedback assisted bone drilling with high quality and, thus, will have a great potential in developing medical applications.
Barrett's esophagus denotes a disorder in the digestive system that affects the esophagus' mucosal cells, causing reflux, and showing potential convergence to esophageal adenocarcinoma if not treated in initial stages. Thus, fast and reliable computer-aided diagnosis becomes considerably welcome. Nevertheless, such approaches usually suffer from imbalanced datasets, which can be addressed through Generative Adversarial Networks (GANs). Such techniques generate realistic images based on observed samples, even though at the cost of a proper selection of its hyperparameters. Many works employed a class of nature-inspired algorithms called metaheuristics to tackle the problem considering distinct deep learning approaches. Therefore, this paper's main contribution is to introduce metaheuristic techniques to fine-tune GANs in the context of Barrett's esophagus identification, as well as to investigate the feasibility of generating high-quality synthetic images for early-cancer assisted identification.
We investigate contrastive learning in a multi-task learning setting classifying and segmenting early Barrett’s cancer. How can contrastive learning be applied in a domain with few classes and low inter-class and inter-sample variance, potentially enabling image retrieval or image attribution? We introduce a data sampling strategy that mines per-lesion data for positive samples and keeps a queue of the recent projections as negative samples. We propose a masking strategy for the NT-Xent loss that keeps the negative set pure and removes samples from the same lesion. We show cohesion and uniqueness improvements of the proposed method in feature space. The introduction of the auxiliary objective does not affect the performance but adds the ability to indicate similarity between lesions. Therefore, the approach could enable downstream auto-documentation tasks on homogeneous medical image data.
Evaluierung von Registrierungsstrategien zur multimodalen 3D-Rekonstruktion von Rattenhirnschnitten
(2006)
In dieser Arbeit werden drei Strategien zur 3D Stapelung von multimodalen Schnittbildern vorgestellt. Die Strategien werden experimentell anhand von Dualtracer-Autoradiographien evaluiert. Dazu werden neue Maße zur Beschreibung der Konsistenz innerhalb einer Modalität und der Konsistenz der Modalitäten untereinander entwickelt, die auf bekannten Registrierungsmetriken basieren. Gerade bezüglich der Konsistenz der Modalitäten untereinander zeigen zwei Strategien die besten Resultate: (1) abwechselnde multimodale Registrierung (2) monomodale Rekonstruktion einer Modalität und multimodale 2D Registrierung der zweiten Modalität.
In neuroscience, small-animal studies frequently involve dealing with series of images from multiple modalities such as histology and autoradiography. The consistent and bias-free restacking of multi-modality image series is obligatory as a starting point for subsequent non-rigid registration procedures and for quantitative comparisons with positron emission tomography (PET) and other in vivo data. Up to now, consistency between 2D slices without cross validation using an inherent 3D modality is frequently presumed to be close to the true morphology due to the smooth appearance of the contours of anatomical structures. However, in multi-modality stacks consistency is difficult to assess. In this work, consistency is defined in terms of smoothness of neighboring slices within a single modality and between different modalities. Registration bias denotes the distortion of the registered stack in comparison to the true 3D morphology and shape. Based on these metrics, different restacking strategies of multi-modality rat brain slices are experimentally evaluated. Experiments based on MRI-simulated and real dual-tracer autoradiograms reveal a clear bias of the restacked volume despite quantitatively high consistency and qualitatively smooth brain structures. However, different registration strategies yield different inter-consistency metrics. If no genuine 3D modality is available, the use of the so-called SOP (slice-order preferred) or MOSOP (modality-and-slice-order preferred) strategy is recommended.
Semantic segmentation is an essential task in medical imaging research. Many powerful deep-learning-based approaches can be employed for this problem, but they are dependent on the availability of an expansive labeled dataset. In this work, we augment such supervised segmentation models to be suitable for learning from unlabeled data. Our semi-supervised approach, termed Error-Correcting Mean-Teacher, uses an exponential moving average model like the original Mean Teacher but introduces our new paradigm of error correction. The original segmentation network is augmented to handle this secondary correction task. Both tasks build upon the core feature extraction layers of the model. For the correction task, features detected in the input image are fused with features detected in the predicted segmentation and further processed with task-specific decoder layers. The combination of image and segmentation features allows the model to correct present mistakes in the given input pair. The correction task is trained jointly on the labeled data. On unlabeled data, the exponential moving average of the original network corrects the student’s prediction. The combined outputs of the students’ prediction with the teachers’ correction form the basis for the semi-supervised update. We evaluate our method with the 2017 and 2018 Robotic Scene Segmentation data, the ISIC 2017 and the BraTS 2020 Challenges, a proprietary Endoscopic Submucosal Dissection dataset, Cityscapes, and Pascal VOC 2012. Additionally, we analyze the impact of the individual components and examine the behavior when the amount of labeled data varies, with experiments performed on two distinct segmentation architectures. Our method shows improvements in terms of the mean Intersection over Union over the supervised baseline and competing methods. Code is available at https://github.com/CloneRob/ECMT.
For accurate registration of histological sections blockface images are frequently used as three dimensional reference. However, due to the use of endocentric lenses the images suffer from perspective errors such as scaling and seemingly relative movement of planes which are located in different distances parallel to the imaging sensor. The suggested correction of those errors is based on the estimation of scaling factors derived from image registration of regions characterized by differing distances to the point of view in neighboring sections. The correction allows the generation of a consistent three dimensional blockface volume.
Background and aims: The accurate differentiation between T1a and T1b Barrett’s cancer has both therapeutic and prognostic implications but is challenging even for experienced physicians. We trained an Artificial Intelligence (AI) system on the basis of deep artificial neural networks (deep learning) to differentiate between T1a and T1b Barrett’s cancer white-light images.
Methods: Endoscopic images from three tertiary care centres in Germany were collected retrospectively. A deep learning system was trained and tested using the principles of cross-validation. A total of 230 white-light endoscopic images (108 T1a and 122 T1b) was evaluated with the AI-system. For comparison, the images were also classified by experts specialized in endoscopic diagnosis and treatment of Barrett’s cancer.
Results: The sensitivity, specificity, F1 and accuracy of the AI-system in the differentiation between T1a and T1b cancer lesions was 0.77, 0.64, 0.73 and 0.71, respectively. There was no statistically significant difference between the performance of the AI-system and that of human experts with sensitivity, specificity, F1 and accuracy of 0.63, 0.78, 0.67 and 0.70 respectively.
Conclusion: This pilot study demonstrates the first multicenter application of an AI-based system in the prediction of submucosal invasion in endoscopic images of Barrett’s cancer. AI scored equal to international experts in the field, but more work is necessary to improve the system and apply it to video sequences and in a real-life setting. Nevertheless, the correct prediction of submucosal invasion in Barret´s cancer remains challenging for both experts and AI.
Aims
Eosinophilic esophagitis (EoE) is easily missed during endoscopy, either because physicians are not familiar with its endoscopic features or the morphologic changes are too subtle. In this preliminary paper, we present the first attempt to detect EoE in endoscopic white light (WL) images using a deep learning network (EoE-AI).
Methods
401 WL images of eosinophilic esophagitis and 871 WL images of normal esophageal mucosa were evaluated. All images were assessed for the Endoscopic Reference score (EREFS) (edema, rings, exudates, furrows, strictures). Images with strictures were excluded. EoE was defined as the presence of at least 15 eosinophils per high power field on biopsy. A convolutional neural network based on the ResNet architecture with several five-fold cross-validation runs was used. Adding auxiliary EREFS-classification branches to the neural network allowed the inclusion of the scores as optimization criteria during training. EoE-AI was evaluated for sensitivity, specificity, and F1-score. In addition, two human endoscopists evaluated the images.
Results
EoE-AI showed a mean sensitivity, specificity, and F1 of 0.759, 0.976, and 0.834 respectively, averaged over the five distinct cross-validation runs. With the EREFS-augmented architecture, a mean sensitivity, specificity, and F1-score of 0.848, 0.945, and 0.861 could be demonstrated respectively. In comparison, the two human endoscopists had an average sensitivity, specificity, and F1-score of 0.718, 0.958, and 0.793.
Conclusions
To the best of our knowledge, this is the first application of deep learning to endoscopic images of EoE which were also assessed after augmentation with the EREFS-score. The next step is the evaluation of EoE-AI using an external dataset. We then plan to assess the EoE-AI tool on endoscopic videos, and also in real-time. This preliminary work is encouraging regarding the ability for AI to enhance physician detection of EoE, and potentially to do a true “optical biopsy” but more work is needed.
Einleitung
Die sichere Detektion und Charakterisierung von Barrett-Ösophagus assoziierten Neoplasien (BERN) stellt selbst für erfahrene Endoskopiker eine Herausforderung dar.
Ziel
Ziel dieser Studie ist es, den Add-on Effekt eines künstlichen Intelligenz (KI) Systems (Barrett-Ampel) als Entscheidungsunterstüzungssystem für Endoskopiker ohne Expertise bei der Untersuchung von BERN zu evaluieren.
Material und Methodik
Zwölf Videos in „Weißlicht“ (WL), „narrow-band imaging“ (NBI) und „texture and color enhanced imaging“ (TXI) von histologisch bestätigten Barrett-Metaplasien oder BERN wurden von Experten und Untersuchern ohne Barrett-Expertise evaluiert. Die Probanden wurden dazu aufgefordert in den Videos auftauchende BERN zu identifizieren und gegebenenfalls die optimale Biopsiestelle zu markieren. Unser KI-System wurde demselben Test unterzogen, wobei dieses BERN in Echtzeit segmentierte und farblich von umliegendem Epithel differenzierte. Anschließend wurden den Probanden die Videos mit zusätzlicher KI-Unterstützung gezeigt. Basierend auf dieser neuen Information, wurden die Probanden zu einer Reevaluation ihrer initialen Beurteilung aufgefordert.
Ergebnisse
Die „Barrett-Ampel“ identifizierte unabhängig von den verwendeten Darstellungsmodi (WL, NBI, TXI) alle BERN. Zwei entzündlich veränderte Läsionen wurden fehlinterpretiert (Genauigkeit=75%). Während Experten vergleichbare Ergebnisse erzielten (Genauigkeit=70,8%), hatten Endoskopiker ohne Expertise bei der Beurteilung von Barrett-Metaplasien eine Genauigkeit von lediglich 58,3%. Wurden die nicht-Experten allerdings von unserem KI-System unterstützt, erreichten diese eine Genauigkeit von 75%.
Zusammenfassung
Unser KI-System hat das Potential als Entscheidungsunterstützungssystem bei der Differenzierung zwischen Barrett-Metaplasie und BERN zu fungieren und so Endoskopiker ohne entsprechende Expertise zu assistieren. Eine Limitation dieser Studie ist die niedrige Anzahl an eingeschlossenen Videos. Um die Ergebnisse dieser Studie zu bestätigen, müssen randomisierte kontrollierte klinische Studien durchgeführt werden.
Einleitung
Die Differenzierung zwischen nicht dysplastischem Barrett-Ösophagus (NDBE) und mit Barrett-Ösophagus assoziierten Neoplasien (BERN) während der endoskopischen Inspektion erfordert viel Expertise. Die frühe Diagnosestellung ist wichtig für die weitere Prognose des Barrett-Karzinoms. In Deutschland werden Patient:innen mit einem Barrett-Ösophagus (BE) in der Regel im niedergelassenen Sektor überwacht.
Ziele
Ziel ist es, den Einfluss von einem auf Künstlicher Intelligenz (KI) basierenden klinischen Entscheidungsunterstützungssystems (CDSS) auf die Performance von niedergelassenen Gastroenterolog:innen (NG) bei der Evaluation von Barrett-Ösophagus (BE) zu untersuchen.
Methodik
Es erfolgte die prospektive Sammlung von 96 unveränderten hochauflösenden Videos mit Fällen von Patient:innen mit histologisch bestätigtem NDBE und BERN. Alle eingeschlossenen Fälle enthielten mindestens zwei der folgenden Darstellungsmethoden: HD-Weißlichtendoskopie, Narrow Band Imaging oder Texture and Color Enhancement Imaging. Sechs NG von sechs unterschiedlichen Praxen wurden als Proband:innen eingeschlossen. Es erfolgte eine permutierte Block-Randomisierung der Videofälle in entweder Gruppe A oder Gruppe B. Gruppe A implizierte eine Evaluation des Falls durch Proband:innen zunächst ohne KI und anschließend mit KI als CDSS. In Gruppe B erfolgte die Evaluation in umgekehrter Reihenfolge. Anschließend erfolgte eine zufällige Wiedergabe der so entstandenen Subgruppen im Rahmen des Tests.
Ergebnis
In diesem Test konnte ein von uns entwickeltes KI-System (Barrett-Ampel) eine Sensitivität von 92,2%, eine Spezifität von 68,9% und eine Accuracy von 81,3% erreichen. Mit der Hilfe von KI verbesserte sich die Sensitivität der NG von 64,1% auf 71,2% (p<0,001) und die Accuracy von 66,3% auf 70,8% (p=0,006) signifikant. Eine signifikante Verbesserung dieser Parameter zeigte sich ebenfalls, wenn die Proband:innen die Fälle zunächst ohne KI evaluierten (Gruppe A). Wurde der Fall jedoch als Erstes mit der Hilfe von KI evaluiert (Gruppe B), blieb die Performance nahezu konstant.
Schlussfolgerung
Es konnte ein performantes KI-System zur Evaluation von BE entwickelt werden. NG verbessern sich bei der Evaluation von BE durch den Einsatz von KI.
Einleitung
Third space Endoskopieprozeduren wie die endoskopische Submukosadissektion (ESD) und die perorale endoskopische Myotomie (POEM) sind technisch anspruchsvoll und gehen mit untersucherabhängigen Komplikationen wie Blutungen und Perforationen einher. Grund hierfür ist die unabsichtliche Durchschneidung von submukosalen Blutgefäßen ohne präemptive Koagulation.
Ziele
Die Forschungsfrage, ob ein KI-Algorithmus die intraprozedurale Gefäßerkennung bei ESD und POEM unterstützen und damit Komplikationen wie Blutungen verhindern könnte, erscheint in Anbetracht des erfolgreichen Einsatzes von KI bei der Erkennung von Kolonpolypen interessant.
Methoden
Auf 5470 Einzelbildern von 59 third space Endoscopievideos wurden submukosale Blutgefäße annotiert. Zusammen mit weiteren 179.681 nicht-annotierten Bildern wurde ein DeepLabv3+neuronales Netzwerk mit dem ECMT-Verfahren für semi-supervised learning trainiert, um Blutgefäße in Echtzeit erkennen zu können. Für die Evaluation wurde ein Videotest mit 101 Videoclips aus 15 vom Trainingsdatensatz separaten Prozeduren mit 200 vordefinierten Gefäßen erstellt. Die Gefäßdetektionsrate, -zeit und -dauer, definiert als der Prozentsatz an Einzelbildern eines Videos bezogen auf den Goldstandard, auf denen ein definiertes Gefäß erkannt wurde, wurden erhoben. Acht erfahrene Endoskopiker wurden mithilfe dieses Videotests im Hinblick auf Gefäßdetektion getestet, wobei eine Hälfte der Videos nativ, die andere Hälfte nach Markierung durch den KI-Algorithmus angesehen wurde.
Ergebnisse
Der mittlere Dice Score des Algorithmus für Blutgefäße war 68%. Die mittlere Gefäßdetektionsrate im Videotest lag bei 94% (96% für ESD; 74% für POEM). Die mediane Gefäßdetektionszeit des Algorithmus lag bei 0,32 Sekunden (0,3 Sekunden für ESD; 0,62 Sekunden für POEM). Die mittlere Gefäßdetektionsdauer lag bei 59,1% (60,6% für ESD; 44,8% für POEM) des Goldstandards. Alle Endoskopiker hatten mit KI-Unterstützung eine höhere Gefäßdetektionsrate als ohne KI. Die mittlere Gefäßdetektionsrate ohne KI lag bei 56,4%, mit KI bei 71,2% (p<0.001).
Schlussfolgerung
KI-Unterstützung war mit einer statistisch signifikant höheren Gefäßdetektionsrate vergesellschaftet. Die mediane Gefäßdetektionszeit von deutlich unter einer Sekunde sowie eine Gefäßdetektionsdauer von größer 50% des Goldstandards wurden für den klinischen Einsatz als ausreichend erachtet. In prospektiven Anwendungsstudien sollte der KI-Algorithmus auf klinische Relevanz getestet werden.
Laryngoscopic images of the vocal tract are used for diagnostic purposes. Quantitative mea-surements like changes of the glottis size or the surface of the vocal cords during an image sequence can be helpful to describe the healing process or to compare the findings of diffe-rent patients. Typically the endoscopic images are circulary symmetric distorted (barrel di-stortion). Therefore measurements of geometric dimensions depend on the object´s position in the image. In this paper an algorithm is presented which allows the computation of the translational invariant "real" object size by correcting the image distortion without using additional calibration of the optical environment.
Background and aims
Celiac disease with its endoscopic manifestation of villous atrophy is underdiagnosed worldwide. The application of artificial intelligence (AI) for the macroscopic detection of villous atrophy at routine esophagogastroduodenoscopy may improve diagnostic performance.
Methods
A dataset of 858 endoscopic images of 182 patients with villous atrophy and 846 images from 323 patients with normal duodenal mucosa was collected and used to train a ResNet 18 deep learning model to detect villous atrophy. An external data set was used to test the algorithm, in addition to six fellows and four board certified gastroenterologists. Fellows could consult the AI algorithm’s result during the test. From their consultation distribution, a stratification of test images into “easy” and “difficult” was performed and used for classified performance measurement.
Results
External validation of the AI algorithm yielded values of 90 %, 76 %, and 84 % for sensitivity, specificity, and accuracy, respectively. Fellows scored values of 63 %, 72 % and 67 %, while the corresponding values in experts were 72 %, 69 % and 71 %, respectively. AI consultation significantly improved all trainee performance statistics. While fellows and experts showed significantly lower performance for “difficult” images, the performance of the AI algorithm was stable.
Conclusion
In this study, an AI algorithm outperformed endoscopy fellows and experts in the detection of villous atrophy on endoscopic still images. AI decision support significantly improved the performance of non-expert endoscopists. The stable performance on “difficult” images suggests a further positive add-on effect in challenging cases.
Aims
Celiac disease (CD) is a complex condition caused by an autoimmune reaction to ingested gluten. Due to its polymorphic manifestation and subtle endoscopic presentation, the diagnosis is difficult and thus the disorder is underreported. We aimed to use deep learning to identify celiac disease on endoscopic images of the small bowel.
Methods
Patients with small intestinal histology compatible with CD (MARSH classification I-III) were extracted retrospectively from the database of Augsburg University hospital. They were compared to patients with no clinical signs of CD and histologically normal small intestinal mucosa. In a first step MARSH III and normal small intestinal mucosa were differentiated with the help of a deep learning algorithm. For this, the endoscopic white light images were divided into five equal-sized subsets. We avoided splitting the images of one patient into several subsets. A ResNet-50 model was trained with the images from four subsets and then validated with the remaining subset. This process was repeated for each subset, such that each subset was validated once. Sensitivity, specificity, and harmonic mean (F1) of the algorithm were determined.
Results
The algorithm showed values of 0.83, 0.88, and 0.84 for sensitivity, specificity, and F1, respectively. Further data showing a comparison between the detection rate of the AI model and that of experienced endoscopists will be available at the time of the upcoming conference.
Conclusions
We present the first clinical report on the use of a deep learning algorithm for the detection of celiac disease using endoscopic images. Further evaluation on an external data set, as well as in the detection of CD in real-time, will follow. However, this work at least suggests that AI can assist endoscopists in the endoscopic diagnosis of CD, and ultimately may be able to do a true optical biopsy in live-time.
Limitations in computer-assisted diagnosis include lack of labeled data and inability to model the relation between what experts see and what computers learn. Even though artificial intelligence and machine learning have demonstrated remarkable performances in medical image computing, their accountability and transparency level must be improved to transfer this success into clinical practice. The reliability of machine learning decisions must be explained and interpreted, especially for supporting the medical diagnosis. While deep learning techniques are broad so that unseen information might help learn patterns of interest, human insights to describe objects of interest help in decision-making. This paper proposes a novel approach, DeepCraftFuse, to address the challenge of combining information provided by deep networks with visual-based features to significantly enhance the correct identification of cancerous tissues in patients affected with Barrett’s esophagus (BE). We demonstrate that DeepCraftFuse outperforms state-of-the-art techniques on private and public datasets, reaching results of around 95% when distinguishing patients affected by BE that is either positive or negative to esophageal cancer.
BACKGROUND
Differentiation of high-flow from low-flow vascular malformations (VMs) is crucial for therapeutic management of this orphan disease.
OBJECTIVE
A convolutional neural network (CNN) was evaluated for differentiation of peripheral vascular malformations (VMs) on T2-weighted short tau inversion recovery (STIR) MRI.
METHODS
527 MRIs (386 low-flow and 141 high-flow VMs) were randomly divided into training, validation and test set for this single-center study. 1) Results of the CNN's diagnostic performance were compared with that of two expert and four junior radiologists. 2) The influence of CNN's prediction on the radiologists' performance and diagnostic certainty was evaluated. 3) Junior radiologists' performance after self-training was compared with that of the CNN.
RESULTS
Compared with the expert radiologists the CNN achieved similar accuracy (92% vs. 97%, p = 0.11), sensitivity (80% vs. 93%, p = 0.16) and specificity (97% vs. 100%, p = 0.50). In comparison to the junior radiologists, the CNN had a higher specificity and accuracy (97% vs. 80%, p < 0.001; 92% vs. 77%, p < 0.001). CNN assistance had no significant influence on their diagnostic performance and certainty. After self-training, the junior radiologists' specificity and accuracy improved and were comparable to that of the CNN.
CONCLUSIONS
Diagnostic performance of the CNN for differentiating high-flow from low-flow VM was comparable to that of expert radiologists. CNN did not significantly improve the simulated daily practice of junior radiologists, self-training was more effective.
Structural MRI brain analysis and segmentation is a crucial part in the daily routine in neurosurgery for intervention planning. Exemplarily, the free software FSL-FAST (FMRIB’s Segmentation Library – FMRIB’s Automated Segmentation Tool) in version 4 is used for segmentation of brain tissue types. To speed up the segmentation procedure by parallel execution, we transferred FSL-FAST to a General Purpose Graphics Processing Unit (GPGPU) using Open Computing Language (OpenCL) [1]. The necessary steps for parallelization resulted in substantially different and less useful results. Therefore, the underlying methods were revised and adapted yielding computational overhead. Nevertheless, we achieved a speed-up factor of 3.59 from CPU to GPGPU execution, as well providing similar useful or even better results.
Training data for Neural Networks is often scarce in the medical domain, which often results in models that struggle to generalize and consequently showpoor performance on unseen datasets. Generally, adding augmentation methods to the training pipeline considerably enhances a model’s performance. Using the dataset of the Foot Ulcer Segmentation Challenge, we analyze two additional augmentation methods in the domain of chronic foot wounds - local warping of wound edges along with projection and blurring of shapes inside wounds. Our experiments show that improvements in the Dice similarity coefficient and Normalized Surface Distance metrics depend on a sensible selection of those augmentation methods.
Despite multimodal treatment, the prognosis of high-grade gliomas is grim. As tumor growth is critically dependent on new blood vessel formation, antiangiogenic treatment approaches offer an innovative treatment strategy. Bevacizumab, a humanized monoclonal antibody, has been in the spotlight of antiangiogenic approaches for several years. Currently, MRI including contrast-enhanced T1-weighted and T2/fluid-attenuated inversion recovery (FLAIR) images is routinely used to evaluate antiangiogenic treatment response (Response Assessment in Neuro-Oncology criteria). However, by restoring the blood–brain barrier, bevacizumab may reduce T1 contrast enhancement and T2/FLAIR hyperintensity, thereby obscuring the imaging-based detection of progression. The aim of this review is to highlight the recent role of imaging biomarkers from MR and PET imaging on measurement of disease progression and treatment effectiveness in antiangiogenic therapies. Based on the reviewed studies, multimodal imaging combining standard MRI with new physiological MRI techniques and metabolic PET imaging, in particular amino acid tracers, may have the ability to detect antiangiogenic drug susceptibility or resistance prior to morphological changes. As advances occur in the development of therapies that target specific biochemical or molecular pathways and alter tumor physiology in potentially predictable ways, the validation of physiological and metabolic imaging biomarkers will become increasingly important in the near future.