TY - CHAP A1 - Palm, Christoph A1 - Scholl, Ingrid A1 - Lehmann, Thomas M. A1 - Spitzer, Klaus ED - Greiser, E. ED - Wischnewsky, M. T1 - Nutzung eines Farbkonstanz-Algorithmus zur Entfernung von Glanzlichtern in laryngoskopischen Bildern T2 - Methoden der Medizinischen Informatik, Biometrie und Epidemiologie in der modernen Informationsgesellschaft N2 - 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. KW - Farbkonstanz KW - Glanzlichtelimination KW - medizinische Bildverarbeitung KW - dichromatisches Reflexionsmodell Y1 - 1998 SN - 9783820813357 SP - 300 EP - 303 PB - MMV Medien und Medizin CY - München ER - TY - JOUR A1 - Palm, Christoph T1 - Color Texture Classification by Integrative Co-Occurrence Matrices JF - Pattern Recognition N2 - Integrative Co-occurrence matrices are introduced as novel features for color texture classification. The extended Co-occurrence notation allows the comparison between integrative and parallel color texture concepts. The information profit of the new matrices is shown quantitatively using the Kolmogorov distance and by extensive classification experiments on two datasets. Applying them to the RGB and the LUV color space the combined color and intensity textures are studied and the existence of intensity independent pure color patterns is demonstrated. The results are compared with two baselines: gray-scale texture analysis and color histogram analysis. The novel features improve the classification results up to 20% and 32% for the first and second baseline, respectively. KW - Color texture KW - Co-occurrence matrix KW - Integrative features KW - KolmogKorov distance KW - Image classification Y1 - 2004 U6 - https://doi.org/10.1016/j.patcog.2003.09.010 VL - 37 IS - 5 SP - 965 EP - 976 ER - TY - GEN A1 - Gräßel, David A1 - Axer, Markus A1 - Palm, Christoph A1 - Dammers, Jürgen A1 - Amunts, Katrin A1 - Pietrzyk, Uwe A1 - Zilles, Karl T1 - Visualization of Fiber Tracts in the Postmortem Human Brain by Means of Polarized Light T2 - NeuroImage KW - Gehirn KW - Bildgebendes Verfahren KW - Polarisiertes Licht KW - Pathologische Anatomie Y1 - 2009 U6 - https://doi.org/10.1016/S1053-8119(09)71415-6 VL - 47 IS - Suppl. 1 SP - 142 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 - 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 - 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 - Axer, Markus A1 - Amunts, Katrin A1 - Gräßel, David A1 - Palm, Christoph A1 - Dammers, Jürgen A1 - Axer, Hubertus A1 - Pietrzyk, Uwe A1 - Zilles, Karl T1 - Novel Approach to the Human Connectome BT - Ultra-High Resolution Mapping of Fiber Tracts in the Brain JF - NeuroImage N2 - 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. KW - Connectome KW - Human brain KW - Method KW - Polarized light imaging KW - Tractography KW - Systems biology KW - Bildgebendes Verfahren KW - Dreidimensionale Bildverarbeitung KW - Polarisiertes Licht KW - Gehirnkarte Y1 - 2011 U6 - https://doi.org/10.1016/j.neuroimage.2010.08.075 VL - 54 IS - 2 SP - 1091 EP - 1101 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 - 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 - Kolev, Kalin A1 - Kirchgeßner, Norbert A1 - Houben, Sebastian A1 - Csiszár, Agnes A1 - Rubner, Wolfgang A1 - Palm, Christoph A1 - Eiben, Björn A1 - Merkel, Rudolf A1 - Cremers, Daniel T1 - A variational approach to vesicle membrane reconstruction from fluorescence imaging JF - Pattern Recognition N2 - Biological applications like vesicle membrane analysis involve the precise segmentation of 3D structures in noisy volumetric data, obtained by techniques like magnetic resonance imaging (MRI) or laser scanning microscopy (LSM). Dealing with such data is a challenging task and requires robust and accurate segmentation methods. In this article, we propose a novel energy model for 3D segmentation fusing various cues like regional intensity subdivision, edge alignment and orientation information. The uniqueness of the approach consists in the definition of a new anisotropic regularizer, which accounts for the unbalanced slicing of the measured volume data, and the generalization of an efficient numerical scheme for solving the arising minimization problem, based on linearization and fixed-point iteration. We show how the proposed energy model can be optimized globally by making use of recent continuous convex relaxation techniques. The accuracy and robustness of the presented approach are demonstrated by evaluating it on multiple real data sets and comparing it to alternative segmentation methods based on level sets. Although the proposed model is designed with focus on the particular application at hand, it is general enough to be applied to a variety of different segmentation tasks. KW - 3D segmentation KW - Convex optimization KW - Vesicle membrane analysis KW - Fluorescence imaging KW - Dreidimensionale Bildverarbeitung KW - Bildsegmentierung KW - Konvexe Optimierung Y1 - 2011 U6 - https://doi.org/10.1016/j.patcog.2011.04.019 VL - 44 IS - 12 SP - 2944 EP - 2958 PB - Elsevier 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 - CHAP A1 - Weber, Joachim A1 - Doenitz, Christian A1 - Brawanski, Alexander A1 - Palm, Christoph T1 - Data-Parallel MRI Brain Segmentation in Clinicial Use BT - Porting FSL-Fastv4 to GPGPUs T2 - Bildverarbeitung für die Medizin 2015; Algorithmen - Systeme - Anwendungen; Proceedings des Workshops vom 15. bis 17. März 2015 in Lübeck N2 - 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. KW - Brain Segmentation KW - Magnetic Resonance Imaging KW - Parallel Execution KW - Voxel Spacing KW - General Purpose Graphic Processing Unit KW - Kernspintomografie KW - Gehirn KW - Bildsegmentierung KW - Parallelverarbeitung Y1 - 2015 U6 - https://doi.org/10.1007/978-3-662-46224-9_67 SP - 389 EP - 394 PB - Springer CY - Berlin ER - TY - CHAP A1 - Szalo, Alexander Eduard A1 - Zehner, Alexander A1 - Palm, Christoph T1 - GraphMIC: Medizinische Bildverarbeitung in der Lehre T2 - Bildverarbeitung für die Medizin 2015; Algorithmen - Systeme - Anwendungen; Proceedings des Workshops vom 15. bis 17. März 2015 in Lübeck N2 - 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. KW - Bildverarbeitung KW - Medizin KW - Hochschuldidaktik Y1 - 2015 U6 - https://doi.org/10.1007/978-3-662-46224-9_68 SP - 395 EP - 400 PB - Springer CY - Berlin ER - TY - CHAP A1 - Wöhl, Rebecca A1 - Huber, Michaela A1 - Loibl, Markus A1 - Riebschläger, Birgit A1 - Nerlich, Michael A1 - Palm, Christoph T1 - The Impact of Semi-Automated Segmentation and 3D Analysis on Testing New Osteosynthesis Material T2 - Bildverarbeitung für die Medizin 2017; Algorithmen - Systeme - Anwendungen; Proceedings des Workshops vom 12. bis 14. März 2017 in Heidelberg N2 - 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. KW - Osteosynthese KW - Implantatwerkstoff KW - Materialprüfung KW - Bildsegmentierung KW - Dreidimensionale Bildverarbeitung Y1 - 2017 U6 - https://doi.org/10.1007/978-3-662-54345-0_30 SP - 122 EP - 127 PB - Springer CY - Berlin ER - TY - CHAP A1 - Mendel, Robert A1 - Ebigbo, Alanna A1 - Probst, Andreas A1 - Messmann, Helmut A1 - Palm, Christoph T1 - Barrett’s Esophagus Analysis Using Convolutional Neural Networks T2 - Bildverarbeitung für die Medizin 2017; Algorithmen - Systeme - Anwendungen. Proceedings des Workshops vom 12. bis 14. März 2017 in Heidelberg N2 - We propose an automatic approach for early detection of adenocarcinoma in the esophagus. High-definition endoscopic images (50 cancer, 50 Barrett) are partitioned into a dataset containing approximately equal amounts of patches showing cancerous and non-cancerous regions. A deep convolutional neural network is adapted to the data using a transfer learning approach. The final classification of an image is determined by at least one patch, for which the probability being a cancer patch exceeds a given threshold. The model was evaluated with leave one patient out cross-validation. With sensitivity and specificity of 0.94 and 0.88, respectively, our findings improve recently published results on the same image data base considerably. Furthermore, the visualization of the class probabilities of each individual patch indicates, that our approach might be extensible to the segmentation domain. KW - Speiseröhrenkrebs KW - Diagnose KW - Maschinelles Lernen KW - Bilderkennung KW - Automatische Klassifikation Y1 - 2017 U6 - https://doi.org/10.1007/978-3-662-54345-0_23 SP - 80 EP - 85 PB - Springer CY - Berlin ER - TY - CHAP A1 - Souza Jr., Luis Antonio de A1 - Hook, Christian A1 - Papa, João Paulo A1 - Palm, Christoph T1 - Barrett's Esophagus Analysis Using SURF Features T2 - Bildverarbeitung für die Medizin 2017; Algorithmen - Systeme - Anwendungen. Proceedings des Workshops vom 12. bis 14. März 2017 in Heidelberg N2 - The development of adenocarcinoma in Barrett’s esophagus is difficult to detect by endoscopic surveillance of patients with signs of dysplasia. Computer assisted diagnosis of endoscopic images (CAD) could therefore be most helpful in the demarcation and classification of neoplastic lesions. In this study we tested the feasibility of a CAD method based on Speeded up Robust Feature Detection (SURF). A given database containing 100 images from 39 patients served as benchmark for feature based classification models. Half of the images had previously been diagnosed by five clinical experts as being ”cancerous”, the other half as ”non-cancerous”. Cancerous image regions had been visibly delineated (masked) by the clinicians. SURF features acquired from full images as well as from masked areas were utilized for the supervised training and testing of an SVM classifier. The predictive accuracy of the developed CAD system is illustrated by sensitivity and specificity values. The results based on full image matching where 0.78 (sensitivity) and 0.82 (specificity) were achieved, while the masked region approach generated results of 0.90 and 0.95, respectively. KW - Speiseröhrenkrankheit KW - Diagnose KW - Maschinelles Sehen KW - Automatische Klassifikation Y1 - 2017 U6 - https://doi.org/10.1007/978-3-662-54345-0_34 SP - 141 EP - 146 PB - Springer CY - Berlin ER - TY - CHAP A1 - Franz, Daniela A1 - Dreher, Maria A1 - Prinzen, Martin A1 - Teßmann, Matthias A1 - Palm, Christoph A1 - Katzky, Uwe A1 - Perret, Jerome A1 - Hofer, Mathias A1 - Wittenberg, Thomas T1 - CT-basiertes virtuelles Fräsen am Felsenbein BT - Bild- und haptischen Wiederholfrequenzen bei unterschiedlichen Rendering Methoden T2 - Bildverarbeitung für die Medizin 2018; Algorithmen - Systeme - Anwendungen. Proceedings des Workshops vom 11. bis 13. März 2018 in Erlangen N2 - Im Rahmen der Entwicklung eines haptisch-visuellen Trainingssystems für das Fräsen am Felsenbein werden ein Haptikarm und ein autostereoskopischer 3D-Monitor genutzt, um Chirurgen die virtuelle Manipulation von knöchernen Strukturen im Kontext eines sog. Serious Game zu ermöglichen. Unter anderem sollen Assistenzärzte im Rahmen ihrer Ausbildung das Fräsen am Felsenbein für das chirurgische Einsetzen eines Cochlea-Implantats üben können. Die Visualisierung des virtuellen Fräsens muss dafür in Echtzeit und möglichst realistisch modelliert, implementiert und evaluiert werden. Wir verwenden verschiedene Raycasting Methoden mit linearer und Nearest Neighbor Interpolation und vergleichen die visuelle Qualität und die Bildwiederholfrequenzen der Methoden. Alle verglichenen Verfahren sind sind echtzeitfähig, unterscheiden sich aber in ihrer visuellen Qualität. KW - Felsenbein KW - Fräsen KW - Virtualisierung KW - Computertomographie KW - Computerassistierte Chirurgie Y1 - 2018 SN - 978-3-662-56537-7 U6 - https://doi.org/10.1007/978-3-662-56537-7_51 SP - 176 EP - 181 PB - Springer CY - Berlin ER - TY - JOUR A1 - Wöhl, Rebecca A1 - Maier, Johannes A1 - Gehmert, Sebastian A1 - Palm, Christoph A1 - Riebschläger, Birgit A1 - Nerlich, Michael A1 - Huber, Michaela T1 - 3D Analysis of Osteosyntheses Material using semi-automated CT Segmentation BT - a case series of a 4 corner fusion plate JF - BMC Musculoskeletal Disorders N2 - Backround Scaphoidectomy and midcarpal fusion can be performed using traditional fixation methods like K-wires, staples, screws or different dorsal (non)locking arthrodesis systems. The aim of this study is to test the Aptus four corner locking plate and to compare the clinical findings to the data revealed by CT scans and semi-automated segmentation. Methods: This is a retrospective review of eleven patients suffering from scapholunate advanced collapse (SLAC) or scaphoid non-union advanced collapse (SNAC) wrist, who received a four corner fusion between August 2011 and July 2014. The clinical evaluation consisted of measuring the range of motion (ROM), strength and pain on a visual analogue scale (VAS). Additionally, the Disabilities of the Arm, Shoulder and Hand (QuickDASH) and the Mayo Wrist Score were assessed. A computerized tomography (CT) of the wrist was obtained six weeks postoperatively. After semi-automated segmentation of the CT scans, the models were post processed and surveyed. Results During the six-month follow-up mean range of motion (ROM) of the operated wrist was 60°, consisting of 30° extension and 30° flexion. While pain levels decreased significantly, 54% of grip strength and 89% of pinch strength were preserved compared to the contralateral healthy wrist. Union could be detected in all CT scans of the wrist. While X-ray pictures obtained postoperatively revealed no pathology, two user related technical complications were found through the 3D analysis, which correlated to the clinical outcome. Conclusion Due to semi-automated segmentation and 3D analysis it has been proved that the plate design can keep up to the manufacturers’ promises. Over all, this case series confirmed that the plate can compete with the coexisting techniques concerning clinical outcome, union and complication rate. KW - Handchirurgie KW - Osteosynthese KW - Arthrodese KW - 4FC KW - SLAC wrist KW - SNAC wrist KW - Semi-automated segmentation KW - 3D analysis KW - Computertomographie KW - Bildsegmentierung KW - Dreidimensionale Bildverarbeitung Y1 - 2018 U6 - https://doi.org/10.1186/s12891-018-1975-0 VL - 19 SP - 1 EP - 8 PB - Springer Nature ER - TY - CHAP A1 - Maier, Johannes A1 - Huber, Michaela A1 - Katzky, Uwe A1 - Perret, Jerome A1 - Wittenberg, Thomas A1 - Palm, Christoph T1 - Force-Feedback-assisted Bone Drilling Simulation Based on CT Data T2 - Bildverarbeitung für die Medizin 2018; Algorithmen - Systeme - Anwendungen; Proceedings des Workshops vom 11. bis 13. März 2018 in Erlangen N2 - 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. KW - Handchirurgie KW - Osteosynthese KW - Simulation KW - Lernprogramm Y1 - 2018 U6 - https://doi.org/10.1007/978-3-662-56537-7_78 SP - 291 EP - 296 PB - Springer CY - Berlin ER - TY - JOUR A1 - Graßmann, Felix A1 - Mengelkamp, Judith A1 - Brandl, Caroline A1 - Harsch, Sebastian A1 - Zimmermann, Martina E. A1 - Linkohr, Birgit A1 - Peters, Annette A1 - Heid, Iris M. A1 - Palm, Christoph A1 - Weber, Bernhard H. F. T1 - A Deep Learning Algorithm for Prediction of Age-Related Eye Disease Study Severity Scale for Age-Related Macular Degeneration from Color Fundus Photography JF - Ophtalmology N2 - Purpose Age-related macular degeneration (AMD) is a common threat to vision. While classification of disease stages is critical to understanding disease risk and progression, several systems based on color fundus photographs are known. Most of these require in-depth and time-consuming analysis of fundus images. Herein, we present an automated computer-based classification algorithm. Design Algorithm development for AMD classification based on a large collection of color fundus images. Validation is performed on a cross-sectional, population-based study. Participants. We included 120 656 manually graded color fundus images from 3654 Age-Related Eye Disease Study (AREDS) participants. AREDS participants were >55 years of age, and non-AMD sight-threatening diseases were excluded at recruitment. In addition, performance of our algorithm was evaluated in 5555 fundus images from the population-based Kooperative Gesundheitsforschung in der Region Augsburg (KORA; Cooperative Health Research in the Region of Augsburg) study. Methods. We defined 13 classes (9 AREDS steps, 3 late AMD stages, and 1 for ungradable images) and trained several convolution deep learning architectures. An ensemble of network architectures improved prediction accuracy. An independent dataset was used to evaluate the performance of our algorithm in a population-based study. Main Outcome Measures. κ Statistics and accuracy to evaluate the concordance between predicted and expert human grader classification. Results. A network ensemble of 6 different neural net architectures predicted the 13 classes in the AREDS test set with a quadratic weighted κ of 92% (95% confidence interval, 89%–92%) and an overall accuracy of 63.3%. In the independent KORA dataset, images wrongly classified as AMD were mainly the result of a macular reflex observed in young individuals. By restricting the KORA analysis to individuals >55 years of age and prior exclusion of other retinopathies, the weighted and unweighted κ increased to 50% and 63%, respectively. Importantly, the algorithm detected 84.2% of all fundus images with definite signs of early or late AMD. Overall, 94.3% of healthy fundus images were classified correctly. Conclusions Our deep learning algoritm revealed a weighted κ outperforming human graders in the AREDS study and is suitable to classify AMD fundus images in other datasets using individuals >55 years of age. KW - Senile Makuladegeneration KW - Krankheitsverlauf KW - Mustererkennung KW - Maschinelles Lernen Y1 - 2018 U6 - https://doi.org/10.1016/j.ophtha.2018.02.037 N1 - Corresponding authors: Bernhard H. F. Weber, University of Regensburg, and Christoph Palm VL - 125 IS - 9 SP - 1410 EP - 1420 PB - Elsevier ER -