TY - JOUR A1 - Ebigbo, Alanna A1 - Mendel, Robert A1 - Scheppach, Markus W. A1 - Probst, Andreas A1 - Shahidi, Neal A1 - Prinz, Friederike A1 - Fleischmann, Carola A1 - Römmele, Christoph A1 - Gölder, Stefan Karl A1 - Braun, Georg A1 - Rauber, David A1 - Rückert, Tobias A1 - Souza Jr., Luis Antonio de A1 - Papa, João Paulo A1 - Byrne, Michael F. A1 - Palm, Christoph A1 - Messmann, Helmut T1 - Vessel and tissue recognition during third-space endoscopy using a deep learning algorithm JF - Gut N2 - 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. KW - Artificial Intelligence KW - Endoscopy KW - Medical Image Computing Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:898-opus4-54293 VL - 71 IS - 12 SP - 2388 EP - 2390 PB - BMJ CY - London ER - TY - JOUR A1 - Ott, Tankred A1 - Palm, Christoph A1 - Vogt, Robert A1 - Oberprieler, Christoph T1 - GinJinn: An object-detection pipeline for automated feature extraction from herbarium specimens JF - Applications in Plant Sciences N2 - 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. KW - Deep Learning KW - herbarium specimens KW - object detection KW - visual recognition KW - Deep Learning KW - Objekterkennung KW - Maschinelles Sehen KW - Pflanzen Y1 - 2020 U6 - https://doi.org/10.1002/aps3.11351 SN - 2168-0450 VL - 8 IS - 6 SP - e11351 PB - Wiley, Botanical Society of America ER - TY - JOUR A1 - Meinikheim, Michael A1 - Mendel, Robert A1 - Palm, Christoph A1 - Probst, Andreas A1 - Muzalyova, Anna A1 - Scheppach, Markus W. A1 - Nagl, Sandra A1 - Schnoy, Elisabeth A1 - Römmele, Christoph A1 - Schulz, Dominik Andreas Helmut Otto A1 - Schlottmann, Jakob A1 - Prinz, Friederike A1 - Rauber, David A1 - Rückert, Tobias A1 - Matsumura, Tomoaki A1 - Fernández-Esparrach, Glòria A1 - Parsa, Nasim A1 - Byrne, Michael F. A1 - Messmann, Helmut A1 - Ebigbo, Alanna T1 - Influence of artificial intelligence on the diagnostic performance of endoscopists in the assessment of Barrett’s esophagus: a tandem randomized and video trial JF - Endoscopy N2 - Background This study evaluated the effect of an artificial intelligence (AI)-based clinical decision support system on the performance and diagnostic confidence of endoscopists in their assessment of Barrett’s esophagus (BE). Methods 96 standardized endoscopy videos were assessed by 22 endoscopists with varying degrees of BE experience from 12 centers. Assessment was randomized into two video sets: group A (review first without AI and second with AI) and group B (review first with AI and second without AI). Endoscopists were required to evaluate each video for the presence of Barrett’s esophagus-related neoplasia (BERN) and then decide on a spot for a targeted biopsy. After the second assessment, they were allowed to change their clinical decision and confidence level. Results AI had a stand-alone sensitivity, specificity, and accuracy of 92.2%, 68.9%, and 81.3%, respectively. Without AI, BE experts had an overall sensitivity, specificity, and accuracy of 83.3%, 58.1%, and 71.5%, respectively. With AI, BE nonexperts showed a significant improvement in sensitivity and specificity when videos were assessed a second time with AI (sensitivity 69.8% [95%CI 65.2%–74.2%] to 78.0% [95%CI 74.0%–82.0%]; specificity 67.3% [95%CI 62.5%–72.2%] to 72.7% [95%CI 68.2%–77.3%]). In addition, the diagnostic confidence of BE nonexperts improved significantly with AI. Conclusion BE nonexperts benefitted significantly from additional AI. BE experts and nonexperts remained significantly below the stand-alone performance of AI, suggesting that there may be other factors influencing endoscopists’ decisions to follow or discard AI advice. KW - Artificial Intelligence KW - Endoscopy KW - Medical Image Computing Y1 - 2024 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:898-opus4-72818 VL - 56 SP - 641 EP - 649 PB - Georg Thieme Verlag CY - Stuttgart ER - TY - JOUR A1 - Römmele, Christoph A1 - Mendel, Robert A1 - Barrett, Caroline A1 - Kiesl, Hans A1 - Rauber, David A1 - Rückert, Tobias A1 - Kraus, Lisa A1 - Heinkele, Jakob A1 - Dhillon, Christine A1 - Grosser, Bianca A1 - Prinz, Friederike A1 - Wanzl, Julia A1 - Fleischmann, Carola A1 - Nagl, Sandra A1 - Schnoy, Elisabeth A1 - Schlottmann, Jakob A1 - Dellon, Evan S. A1 - Messmann, Helmut A1 - Palm, Christoph A1 - Ebigbo, Alanna T1 - An artificial intelligence algorithm is highly accurate for detecting endoscopic features of eosinophilic esophagitis JF - Scientific Reports N2 - The endoscopic features associated with eosinophilic esophagitis (EoE) may be missed during routine endoscopy. We aimed to develop and evaluate an Artificial Intelligence (AI) algorithm for detecting and quantifying the endoscopic features of EoE in white light images, supplemented by the EoE Endoscopic Reference Score (EREFS). An AI algorithm (AI-EoE) was constructed and trained to differentiate between EoE and normal esophagus using endoscopic white light images extracted from the database of the University Hospital Augsburg. In addition to binary classification, a second algorithm was trained with specific auxiliary branches for each EREFS feature (AI-EoE-EREFS). The AI algorithms were evaluated on an external data set from the University of North Carolina, Chapel Hill (UNC), and compared with the performance of human endoscopists with varying levels of experience. The overall sensitivity, specificity, and accuracy of AI-EoE were 0.93 for all measures, while the AUC was 0.986. With additional auxiliary branches for the EREFS categories, the AI algorithm (AI-EoEEREFS) performance improved to 0.96, 0.94, 0.95, and 0.992 for sensitivity, specificity, accuracy, and AUC, respectively. AI-EoE and AI-EoE-EREFS performed significantly better than endoscopy beginners and senior fellows on the same set of images. An AI algorithm can be trained to detect and quantify endoscopic features of EoE with excellent performance scores. The addition of the EREFS criteria improved the performance of the AI algorithm, which performed significantly better than endoscopists with a lower or medium experience level. KW - Artificial Intelligence KW - Smart Endoscopy KW - eosinophilic esophagitis Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:898-opus4-46928 VL - 12 PB - Nature Portfolio CY - London ER - TY - JOUR A1 - Roser, David A1 - Meinikheim, Michael A1 - Muzalyova, Anna A1 - Mendel, Robert A1 - Palm, Christoph A1 - Probst, Andreas A1 - Nagl, Sandra A1 - Scheppach, Markus W. A1 - Römmele, Christoph A1 - Schnoy, Elisabeth A1 - Parsa, Nasim A1 - Byrne, Michael F. A1 - Messmann, Helmut A1 - Ebigbo, Alanna T1 - Artificial intelligence-assisted endoscopy and examiner confidence : a study on human–artificial intelligence interaction in Barrett's Esophagus (With Video) JF - DEN Open N2 - Objective Despite high stand-alone performance, studies demonstrate that artificial intelligence (AI)-supported endoscopic diagnostics often fall short in clinical applications due to human-AI interaction factors. This video-based trial on Barrett's esophagus aimed to investigate how examiner behavior, their levels of confidence, and system usability influence the diagnostic outcomes of AI-assisted endoscopy. Methods The present analysis employed data from a multicenter randomized controlled tandem video trial involving 22 endoscopists with varying degrees of expertise. Participants were tasked with evaluating a set of 96 endoscopic videos of Barrett's esophagus in two distinct rounds, with and without AI assistance. Diagnostic confidence levels were recorded, and decision changes were categorized according to the AI prediction. Additional surveys assessed user experience and system usability ratings. Results AI assistance significantly increased examiner confidence levels (p < 0.001) and accuracy. Withdrawing AI assistance decreased confidence (p < 0.001), but not accuracy. Experts consistently reported higher confidence than non-experts (p < 0.001), regardless of performance. Despite improved confidence, correct AI guidance was disregarded in 16% of all cases, and 9% of initially correct diagnoses were changed to incorrect ones. Overreliance on AI, algorithm aversion, and uncertainty in AI predictions were identified as key factors influencing outcomes. The System Usability Scale questionnaire scores indicated good to excellent usability, with non-experts scoring 73.5 and experts 85.6. Conclusions Our findings highlight the pivotal function of examiner behavior in AI-assisted endoscopy. To fully realize the benefits of AI, implementing explainable AI, improving user interfaces, and providing targeted training are essential. Addressing these factors could enhance diagnostic accuracy and confidence in clinical practice. Y1 - 2025 U6 - https://doi.org/10.1002/deo2.70150 VL - 6 IS - 1 PB - Wiley ER - TY - JOUR A1 - Scheppach, Markus W. A1 - Rauber, David A1 - Stallhofer, Johannes A1 - Muzalyova, Anna A1 - Otten, Vera A1 - Manzeneder, Carolin A1 - Schwamberger, Tanja A1 - Wanzl, Julia A1 - Schlottmann, Jakob A1 - Tadic, Vidan A1 - Probst, Andreas A1 - Schnoy, Elisabeth A1 - Römmele, Christoph A1 - Fleischmann, Carola A1 - Meinikheim, Michael A1 - Miller, Silvia A1 - Märkl, Bruno A1 - Stallmach, Andreas A1 - Palm, Christoph A1 - Messmann, Helmut A1 - Ebigbo, Alanna T1 - Detection of duodenal villous atrophy on endoscopic images using a deep learning algorithm JF - Gastrointestinal Endoscopy N2 - 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. KW - celiac disease KW - villous atrophy KW - endoscopy detection KW - artificial intelligence Y1 - 2023 U6 - https://doi.org/10.1016/j.gie.2023.01.006 PB - Elsevier ER - TY - JOUR A1 - Scheppach, Markus W. A1 - Mendel, Robert A1 - Muzalyova, Anna A1 - Rauber, David A1 - Probst, Andreas A1 - Nagl, Sandra A1 - Römmele, Christoph A1 - Yip, Hon Chi A1 - Lau, Louis Ho Shing A1 - Gölder, Stefan Karl A1 - Schmidt, Arthur A1 - Kouladouros, Konstantinos A1 - Abdelhafez, Mohamed A1 - Walter, Benjamin M. A1 - Meinikheim, Michael A1 - Chiu, Philip Wai Yan A1 - Palm, Christoph A1 - Messmann, Helmut A1 - Ebigbo, Alanna T1 - Artificial intelligence improves submucosal vessel detection during third space endoscopy JF - Endoscopy N2 - Background and study aims: While artificial intelligence (AI) shows high potential in decision support for diagnostic gastrointestinal endoscopy, its role in therapeutic endoscopy remains unclear. Third space endoscopic procedures pose the risk of intraprocedural bleeding. Therefore, we aimed to develop an AI algorithm for intraprocedural blood vessel detection. Patients and Methods: Using a test dataset with 101 standardized video clips containing 200 predefined submucosal blood vessels, 19 endoscopists were evaluated for the vessel detection rate (VDR) and time (VDT) with and without support of an AI algorithm. Test subjects were grouped according to experience in ESD. Results: With AI support, endoscopists VDR increased from 56.4% [CI 54.1–58.6] to 72.4% [CI 70.3–74.4]. Endoscopists‘ VDT dropped from 6.7sec [CI 6.2-7.1] to 5.2sec [CI 4.8-5.7]. False positive (FP) readings appeared in 4.5% of frames and were marked significantly shorter than true positives (6.0sec [CI 5.28-6.70] vs. 0.7sec [CI 0.55-0.87]). Conclusions: AI improved the vessel detection rate and time of endoscopists during third space endoscopy. While these data need to be corroborated by clinical trials, AI may prove to be an invaluable tool for the improvement of endoscopic interventions. KW - Artificial Intelligence KW - Third Space Endoscopy Y1 - 2025 U6 - https://doi.org/10.1055/a-2534-1164 PB - Thieme CY - Stuttgart ER - TY - JOUR A1 - Beyer, Thomas A1 - Weigert, Markus A1 - Quick, Harald H. A1 - Pietrzyk, Uwe A1 - Vogt, Florian A1 - Palm, Christoph A1 - Antoch, Gerald A1 - Müller, Stefan P. A1 - Bockisch, Andreas T1 - MR-based attenuation correction for torso-PET/MR imaging BT - pitfalls in mapping MR to CT data JF - European Journal of Nuclear Medicine and Molecular Imaging N2 - 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. KW - PET/MRI KW - PET/CT KW - Attenuation correction KW - Kernspintomografie KW - Positronen-Emissions-Tomografie KW - Schwächung Y1 - 2008 U6 - https://doi.org/10.1007/s00259-008-0734-0 VL - 35 IS - 6 SP - 1142 EP - 1146 ER - TY - JOUR A1 - Hartmann, Robin A1 - Nieberle, Felix A1 - Palm, Christoph A1 - Brébant, Vanessa A1 - Prantl, Lukas A1 - Kuehle, Reinald A1 - Reichert, Torsten E. A1 - Taxis, Juergen A1 - Ettl, Tobias T1 - Utility of Smartphone-based Three-dimensional Surface Imaging for Digital Facial Anthropometry JF - JPRAS Open N2 - 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. KW - Three-dimensional surface imaging KW - Stereophotogrammetry KW - Smartphone-based surface imaging KW - Digital anthropometry KW - Facial anthropometry Y1 - 2024 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:898-opus4-70348 VL - 39 SP - 330 EP - 343 PB - Elsevier ER - TY - JOUR A1 - Knödler, Leonard A1 - Baecher, Helena A1 - Kauke-Navarro, Martin A1 - Prantl, Lukas A1 - Machens, Hans-Günther A1 - Scheuermann, Philipp A1 - Palm, Christoph A1 - Baumann, Raphael A1 - Kehrer, Andreas A1 - Panayi, Adriana C. A1 - Knoedler, Samuel T1 - Towards a Reliable and Rapid Automated Grading System in Facial Palsy Patients: Facial Palsy Surgery Meets Computer Science JF - Journal of Clinical Medicine N2 - 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. Y1 - 2022 U6 - https://doi.org/10.3390/jcm11174998 VL - 11 IS - 17 PB - MDPI CY - Basel ER - TY - JOUR A1 - Souza Jr., Luis Antonio de A1 - Palm, Christoph A1 - Mendel, Robert A1 - Hook, Christian A1 - Ebigbo, Alanna A1 - Probst, Andreas A1 - Messmann, Helmut A1 - Weber, Silke A. T. A1 - Papa, João Paulo T1 - A survey on Barrett's esophagus analysis using machine learning JF - Computers in Biology and Medicine N2 - This work presents a systematic review concerning recent studies and technologies of machine learning for Barrett's esophagus (BE) diagnosis and treatment. The use of artificial intelligence is a brand new and promising way to evaluate such disease. We compile some works published at some well-established databases, such as Science Direct, IEEEXplore, PubMed, Plos One, Multidisciplinary Digital Publishing Institute (MDPI), Association for Computing Machinery (ACM), Springer, and Hindawi Publishing Corporation. Each selected work has been analyzed to present its objective, methodology, and results. The BE progression to dysplasia or adenocarcinoma shows a complex pattern to be detected during endoscopic surveillance. Therefore, it is valuable to assist its diagnosis and automatic identification using computer analysis. The evaluation of the BE dysplasia can be performed through manual or automated segmentation through machine learning techniques. Finally, in this survey, we reviewed recent studies focused on the automatic detection of the neoplastic region for classification purposes using machine learning methods. KW - Speiseröhrenkrankheit KW - Diagnose KW - Mustererkennung KW - Maschinelles Lernen KW - Literaturbericht KW - Barrett's esophagus KW - Machine learning KW - Adenocarcinoma KW - Image processing KW - Pattern recognition KW - Computer-aided diagnosis Y1 - 2018 U6 - https://doi.org/10.1016/j.compbiomed.2018.03.014 VL - 96 SP - 203 EP - 213 PB - Elsevier ER - TY - JOUR A1 - Ebigbo, Alanna A1 - Palm, Christoph A1 - Probst, Andreas A1 - Mendel, Robert A1 - Manzeneder, Johannes A1 - Prinz, Friederike A1 - Souza Jr., Luis Antonio de A1 - Papa, João Paulo A1 - Siersema, Peter A1 - Messmann, Helmut T1 - A technical review of artificial intelligence as applied to gastrointestinal endoscopy: clarifying the terminology JF - Endoscopy International Open N2 - The growing number of publications on the application of artificial intelligence (AI) in medicine underlines the enormous importance and potential of this emerging field of research. In gastrointestinal endoscopy, AI has been applied to all segments of the gastrointestinal tract most importantly in the detection and characterization of colorectal polyps. However, AI research has been published also in the stomach and esophagus for both neoplastic and non-neoplastic disorders. The various technical as well as medical aspects of AI, however, remain confusing especially for non-expert physicians. This physician-engineer co-authored review explains the basic technical aspects of AI and provides a comprehensive overview of recent publications on AI in gastrointestinal endoscopy. Finally, a basic insight is offered into understanding publications on AI in gastrointestinal endoscopy. KW - Diagnose KW - Maschinelles Lernen KW - Gastroenterologie KW - Künstliche Intelligenz KW - Barrett's esophagus KW - Deep learning Y1 - 2019 U6 - https://doi.org/10.1055/a-1010-5705 VL - 07 IS - 12 SP - 1616 EP - 1623 PB - Georg Thieme Verlag CY - Stuttgart ER - TY - JOUR A1 - Weigert, Markus A1 - Pietrzyk, Uwe A1 - Müller, Stefan P. A1 - Palm, Christoph A1 - Beyer, Thomas T1 - Whole-body PET/CT imaging BT - Combining software- and hardware-based co-registration JF - Zeitschrift für Medizinische Physik N2 - 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. KW - PET/CT KW - combined imaging KW - image co-registration KW - attenuation KW - correction KW - Positronen-Emissions-Tomografie KW - Computertomografie KW - Bildgebendes Verfahren KW - Registrierung KW - Schwächung Y1 - 2008 U6 - https://doi.org/10.1016/j.zemedi.2007.07.004 VL - 18 IS - 1 SP - 59 EP - 66 ER - TY - JOUR A1 - Dehnhardt, Markus A1 - Palm, Christoph A1 - Vieten, Andrea A1 - Bauer, Andreas A1 - Pietrzyk, Uwe T1 - Quantifying the A1AR distribution in peritumoral zones around experimental F98 and C6 rat brain tumours JF - Journal of Neuro-Oncology N2 - 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. KW - 3D reconstruction KW - A1 adenosine receptor KW - GBM KW - Kmeans algorithm KW - Brain tumour KW - Receptor autoradiography KW - Hirntumor KW - Dreidimensionale Bildverarbeitung KW - Adenosinrezeptor Y1 - 2007 U6 - https://doi.org/10.1007/s11060-007-9391-6 VL - 85 SP - 49 EP - 63 ER - TY - JOUR A1 - Mang, Andreas A1 - Schnabel, Julia A. A1 - Crum, William R. A1 - Modat, Marc A1 - Camara-Rey, Oscar A1 - Palm, Christoph A1 - Caseiras, Gisele Brasil A1 - Jäger, H. Rolf A1 - Ourselin, Sébastien A1 - Buzug, Thorsten M. A1 - Hawkes, David J. T1 - Consistency of parametric registration in serial MRI studies of brain tumor progression JF - International Journal of Computer Assisted Radiology and Surgery N2 - Object The consistency of parametric registration in multi-temporal magnetic resonance (MR) imaging studies was evaluated. Materials and methods Serial MRI scans of adult patients with a brain tumor (glioma) were aligned by parametric registration. The performance of low-order spatial alignment (6/9/12 degrees of freedom) of different 3D serial MR-weighted images is evaluated. A registration protocol for the alignment of all images to one reference coordinate system at baseline is presented. Registration results were evaluated for both, multimodal intra-timepoint and mono-modal multi-temporal registration. The latter case might present a challenge to automatic intensity-based registration algorithms due to ill-defined correspondences. The performance of our algorithm was assessed by testing the inverse registration consistency. Four different similarity measures were evaluated to assess consistency. Results Careful visual inspection suggests that images are well aligned, but their consistency may be imperfect. Sub-voxel inconsistency within the brain was found for allsimilarity measures used for parametric multi-temporal registration. T1-weighted images were most reliable for establishing spatial correspondence between different timepoints. Conclusions The parametric registration algorithm is feasible for use in this application. The sub-voxel resolution mean displacement error of registration transformations demonstrates that the algorithm converges to an almost identical solution for forward and reverse registration. KW - Inverse registration consistency KW - Parametric serial MR image registration KW - Tumor disease progression KW - Kernspintomografie KW - Registrierung KW - Hirntumor Y1 - 2008 U6 - https://doi.org/10.1007/s11548-008-0234-5 VL - 3 IS - 3-4 SP - 201 EP - 211 ER - TY - JOUR A1 - Deserno, Thomas M. A1 - Handels, Heinz A1 - Maier-Hein, Klaus H. A1 - Mersmann, Sven A1 - Palm, Christoph A1 - Tolxdorff, Thomas A1 - Wagenknecht, Gudrun A1 - Wittenberg, Thomas T1 - Viewpoints on Medical Image Processing BT - From Science to Application JF - Current Medical Imaging Reviews N2 - 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. KW - Medical imaging KW - Image processing KW - Image analysis KW - Vizualization KW - Multi-modal imaging KW - Diffusion-weighted imaging KW - Model-based imaging KW - Digital endoscopy KW - Bildgebendes Verfahren KW - Bildverarbeitung KW - Medizin Y1 - 2013 U6 - https://doi.org/10.2174/1573405611309020002 VL - 9 IS - 2 SP - 79 EP - 88 ER - TY - JOUR A1 - Matusch, Andreas A1 - Depboylu, Candan A1 - Palm, Christoph A1 - Wu, Bei A1 - Höglinger, Günter U. A1 - Schäfer, Martin K.-H. A1 - Becker, Johanna Sabine T1 - Cerebral bio-imaging of Cu, Fe, Zn and Mn in the MPTP mouse model of Parkinsons disease using laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) JF - Journal of the American Society for Mass Spectrometry N2 - Laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) has been established as a powerful technique for the determination of metal and nonmetal distributions within biological systems with high sensitivity. An imaging LA-ICP-MS technique for Fe, Cu, Zn, and Mn was developed to produce large series of quantitative element maps in native brain sections of mice subchronically intoxicated with 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridin (MPTP) as a model of Parkinson’s disease. Images were calibrated using matrix-matched laboratory standards. A software solution allowing a precise delineation of anatomical structures was implemented. Coronal brain sections were analyzed crossing the striatum and the substantia nigra, respectively. Animals sacrificed 2 h, 7 d, or 28 d after the last MPTP injection and controls were investigated. We observed significant decreases of Cu concentrations in the periventricular zone and the fascia dentata at 2 h and 7d and a recovery or overcompensation at 28 d, most pronounced in the rostral periventricular zone (+40%). In the cortex Cu decreased slightly to −10%. Fe increased in the interpeduncular nucleus (+40%) but not in the substantia nigra. This pattern is in line with a differential regulation of periventricular and parenchymal Cu, and with the histochemical localization of Fe, and congruent to regions of preferential MPTP binding described in the rodent brain. The LA-ICP-MS technique yielded valid and statistically robust results in the present study on 39 slices from 19 animals. Our findings underline the value of routine micro-local analytical techniques in the life sciences and affirm a role of Cu availability in Parkinson’s disease. KW - Inductively Couple Plasma Mass Spectrometry KW - Substantia Nigra KW - MPTP KW - Laser Ablation Inductively Couple Plasma Mass Spectrometry KW - MPTP Treatment KW - ICP-Massenspektrometrie KW - Metalle KW - Gehirnkarte KW - MPTP Y1 - 2010 U6 - https://doi.org/10.1016/j.jasms.2009.09.022 VL - 21 IS - 1 SP - 161 EP - 171 ER - TY - JOUR A1 - Becker, Johanna Sabine A1 - Matusch, Andreas A1 - Becker, Julia Susanne A1 - Wu, Bei A1 - Palm, Christoph A1 - Becker, Albert Johann A1 - Salber, Dagmar T1 - Mass spectrometric imaging (MSI) of metals using advanced BrainMet techniques for biomedical research JF - International Journal of Mass Spectrometry N2 - 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. KW - Bioimaging KW - Brain tissue KW - Laser ablation inductively coupled plasma mass spectrometry KW - Laser microdissection inductively coupled plasma mass spectrometry KW - Metals KW - Metallomics KW - Nano-LA-ICP-MS KW - Tumour KW - Massenspektrometrie KW - Bildgebendes Verfahren KW - Metalle KW - Metallproteide KW - Gehirn Y1 - 2011 U6 - https://doi.org/10.1016/j.ijms.2011.01.015 VL - 307 IS - 1-3 SP - 3 EP - 15 PB - eLSEVIER CY - Elsevier ER - TY - JOUR A1 - Becker, Johanna Sabine A1 - Matusch, Andreas A1 - Palm, Christoph A1 - Salber, Dagmar A1 - Morton, Kathryn A. A1 - Becker, Julia Susanne T1 - Bioimaging of metals in brain tissue by laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) and metallomics JF - Metallomics N2 - Laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) has been developed and established as an emerging technique in the generation of quantitative images of metal distributions in thin tissue sections of brain samples (such as human, rat and mouse brain), with applications in research related to neurodegenerative disorders. A new analytical protocol is described which includes sample preparation by cryo-cutting of thin tissue sections and matrix-matched laboratory standards, mass spectrometric measurements, data acquisition, and quantitative analysis. Specific examples of the bioimaging of metal distributions in normal rodent brains are provided. Differences to the normal were assessed in a Parkinson’s disease and a stroke brain model. Furthermore, changes during normal aging were studied. Powerful analytical techniques are also required for the determination and characterization of metal-containing proteins within a large pool of proteins, e.g., after denaturing or non-denaturing electrophoretic separation of proteins in one-dimensional and two-dimensional gels. LA-ICP-MS can be employed to detect metalloproteins in protein bands or spots separated after gel electrophoresis. MALDI-MS can then be used to identify specific metal-containing proteins in these bands or spots. The combination of these techniques is described in the second section. KW - ICP-Massenspektrometrie KW - Metalle KW - Metallproteide KW - Elektrophorese KW - Gehirn Y1 - 2010 U6 - https://doi.org/10.1039/b916722f IS - 2 SP - 104 EP - 111 PB - Oxford Academic Press ER - TY - JOUR A1 - Osterholt, Tobias A1 - Salber, Dagmar A1 - Matusch, Andreas A1 - Becker, Johanna Sabine A1 - Palm, Christoph T1 - IMAGENA: Image Generation and Analysis BT - An Interactive Software Tool handling LA-ICP-MS Data JF - International Journal of Mass Spectrometry N2 - 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. KW - LA-ICP-MS KW - ICP-Massenspektrometrie KW - Bilderzeugung KW - Graphische Benutzeroberfläche KW - Image generation KW - Image analysis KW - Graphical user interface Y1 - 2011 U6 - https://doi.org/10.1016/j.ijms.2011.03.010 VL - 307 IS - 1-3 SP - 232 EP - 239 ER - TY - JOUR A1 - Dammers, Jürgen A1 - Axer, Markus A1 - Gräßel, David A1 - Palm, Christoph A1 - Zilles, Karl A1 - Amunts, Katrin A1 - Pietrzyk, Uwe T1 - Signal enhancement in polarized light imaging by means of independent component analysis JF - NeuroImage N2 - 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. KW - Bildgebendes Verfahren KW - Polarisiertes Licht KW - Signalverarbeitung KW - Signaltrennung KW - Komponentenanalyse KW - Gehirn Y1 - 2010 U6 - https://doi.org/10.1016/j.neuroimage.2009.08.059 VL - 49 IS - 2 SP - 1241 EP - 1248 PB - Elsevier ER - TY - JOUR A1 - Palm, Christoph A1 - Axer, Markus A1 - Gräßel, David A1 - Dammers, Jürgen A1 - Lindemeyer, Johannes A1 - Zilles, Karl A1 - Pietrzyk, Uwe A1 - Amunts, Katrin T1 - Towards ultra-high resolution fibre tract mapping of the human brain BT - registration of polarised light images and reorientation of fibre vectors JF - Frontiers in Human Neuroscience N2 - 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. KW - Bildgebendes Verfahren KW - Dreidimensionale Bildverarbeitung KW - Polarisiertes Licht KW - Gehirnkarte Y1 - 2010 U6 - https://doi.org/10.3389/neuro.09.009.2010 VL - 4 ER - TY - JOUR A1 - Becker, Johanna Sabine A1 - Zoriy, Miroslav A1 - Matusch, Andreas A1 - Wu, Bei A1 - Salber, Dagmar A1 - Palm, Christoph A1 - Becker, Julia Susanne T1 - Bioimaging of Metals by Laser Ablation Inductively Coupled Plasma Mass Spectrometry (LA-ICP-MS) JF - Mass Spectrometry Reviews N2 - The distribution analysis of (essential, beneficial, or toxic) metals (e.g., Cu, Fe, Zn, Pb, and others), metalloids, and non‐metals in biological tissues is of key interest in life science. Over the past few years, the development and application of several imaging mass spectrometric techniques has been rapidly growing in biology and medicine. Especially, in brain research metalloproteins are in the focus of targeted therapy approaches of neurodegenerative diseases such as Alzheimer's and Parkinson's disease, or stroke, or tumor growth. Laser ablation inductively coupled plasma mass spectrometry (LA‐ICP‐MS) using double‐focusing sector field (LA‐ICP‐SFMS) or quadrupole‐based mass spectrometers (LA‐ICP‐QMS) has been successfully applied as a powerful imaging (mapping) technique to produce quantitative images of detailed regionally specific element distributions in thin tissue sections of human or rodent brain. Imaging LA‐ICP‐QMS was also applied to investigate metal distributions in plant and animal sections to study, for example, the uptake and transport of nutrient and toxic elements or environmental contamination. The combination of imaging LA‐ICP‐MS of metals with proteomic studies using biomolecular mass spectrometry identifies metal‐containing proteins and also phosphoproteins. Metal‐containing proteins were imaged in a two‐dimensional gel after electrophoretic separation of proteins (SDS or Blue Native PAGE). Recent progress in LA‐ICP‐MS imaging as a stand‐alone technique and in combination with MALDI/ESI‐MS for selected life science applications is summarized. KW - Bildgebendes Verfahren KW - ICP-Massenspektrometrie KW - Metalle KW - Metallproteide KW - Elektrophorese KW - Gehirnkarte KW - Bioimaging of metals KW - Laser ablation inductively coupled plasma mass spectrometry KW - metal distribution KW - metallomics KW - neurodegenerative diseases Y1 - 2010 U6 - https://doi.org/10.1002/mas.20239 VL - 29 SP - 156 EP - 175 ER - TY - JOUR A1 - Ilgner, Justus F. R. A1 - Palm, Christoph A1 - Schütz, Andreas G. A1 - Spitzer, Klaus A1 - Westhofen, Martin A1 - Lehmann, Thomas M. T1 - Colour Texture Analysis for Quantitative Laryngoscopy JF - Acta Otolaryngologica N2 - Whilst considerable progress has been made in enhancing the quality of indirect laryngoscopy and image processing, the evaluation of clinical findings is still based on the clinician's judgement. The aim of this paper was to examine the feasibility of an objective computer-based method for evaluating laryngeal disease. Digitally recorded images obtained by 90 degree- and 70 degree-angled indirect rod laryngoscopy using standardized white balance values were made of 16 patients and 19 healthy subjects. The digital images were evaluated manually by the clinician based on a standardized questionnaire, and suspect lesions were marked and classified on the image. Following colour separation, normal vocal cord areas as well as suspect lesions were analyzed automatically using co-occurrence matrices, which compare colour differences between neighbouring pixels over a predefined distance. Whilst colour histograms did not provide sufficient information for distinguishing between healthy and diseased tissues, consideration of the blue content of neighbouring pixels enabled a correct classification in 81.4% of cases. If all colour channels (red, green and blue) were regarded simultaneously, the best classification correctness obtained was 77.1%. Although only a very basic classification differentiating between healthy and diseased tissue was attempted, the results showed progress compared to grey-scale histograms, which have been evaluated before. The results document a first step towards an objective, machine-based classification of laryngeal images, which could provide the basis for further development of an expert system for use in indirect laryngoscopy. KW - diagnostic laryngoscopy KW - electronic imaging KW - endoscopy KW - neoplastic larynx disease Y1 - 2003 U6 - https://doi.org/10.1080/00016480310000412 VL - 123 SP - 730 EP - 734 ER - TY - JOUR A1 - Palm, Christoph A1 - Vieten, Andrea A1 - Salber, Dagmar A1 - Pietrzyk, Uwe T1 - Evaluation of Registration Strategies for Multi-modality Images of Rat Brain Slices JF - Physics in Medicine and Biology N2 - 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. KW - Histologie KW - Bildgebendes Verfahren KW - Schnittdarstellung KW - Multimodales Verfahren Y1 - 2009 U6 - https://doi.org/10.1088/0031-9155/54/10/021 VL - 54 IS - 10 SP - 3269 EP - 3289 ER -