TY - CHAP A1 - Schubert, Nicole A1 - Pietrzyk, Uwe A1 - Reißel, Martin A1 - Palm, Christoph T1 - Reduktion von Rissartefakten durch nicht-lineare Registrierung in histologischen Schnittbildern T2 - Bildverarbeitung für die Medizin 2009; Algorithmen - Systeme - Anwendungen; Proceedings des Workshops vom 22. bis 25. März 2009 in Heidelberg N2 - 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. KW - Registrierung KW - Nichtlineare Optimierung KW - Bildsegmentierung KW - Gehirn KW - Schnittdarstellung Y1 - 2009 UR - http://sunsite.informatik.rwth-aachen.de/Publications/CEUR-WS/Vol-446/p410.pdf SP - 410 EP - 414 PB - Springer CY - Berlin 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 - 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 - CHAP A1 - Eiben, Björn A1 - Palm, Christoph A1 - Pietrzyk, Uwe A1 - Davatzikos, Christos A1 - Amunts, Katrin T1 - Error Correction using Registration for Blockface Volume Reconstruction of Serial Histological Sections of the Human Brain T2 - Bildverarbeitung für die Medizin 2010; Algorithmen - Systeme - Anwendungen ; Proceedings des Workshops vom 22. bis 25. März 2009 in Heidelberg N2 - 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. KW - Histologie KW - Diagnostik KW - Bildgebendes Verfahren KW - Schnittdarstellung KW - Fehlerbehandlung Y1 - 2010 UR - http://ceur-ws.org/Vol-574/bvm2010_61.pdf SP - 301 EP - 305 PB - Springer CY - Berlin ER - TY - GEN A1 - Beyer, Thomas A1 - Weigert, Markus A1 - Palm, Christoph A1 - Quick, Harald H. A1 - Müller, Stefan P. A1 - Pietrzyk, Uwe A1 - Vogt, Florian A1 - Martinez, M.J. A1 - Bockisch, Andreas T1 - Towards MR-based attenuation correction for whole-body PET/MR imaging T2 - The Journal of Nuclear Medicine KW - Kernspintomografie KW - Positronen-Emissions-Tomografie KW - Bildgebendes Verfahren KW - Schwächung Y1 - 2006 UR - http://jnm.snmjournals.org/content/47/suppl_1/384P.1.abstract VL - 47 IS - Suppl. 1 SP - 384P 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 - GEN A1 - Weigert, Markus A1 - Beyer, Thomas A1 - Quick, Harald H. A1 - Pietrzyk, Uwe A1 - Palm, Christoph A1 - Müller, Stefan P. T1 - Generation of a MRI reference data set for the validation of automatic, non-rigid image co-registration algorithms T2 - Nuklearmedizin KW - Kernspintomografie KW - Referenzdaten KW - Registrierung KW - Algorithmus Y1 - 2007 VL - 46 IS - 2 SP - A116 ER - TY - CHAP A1 - Pietrzyk, Uwe A1 - Bauer, Dagmar A1 - Vieten, Andrea A1 - Bauer, Andreas A1 - Langen, Karl-Josef A1 - Zilles, Karl A1 - Palm, Christoph T1 - Creating consistent 3D multi-modality data sets from autoradiographic and histological images of the rat brain T2 - IEEE Nuclear Science Symposium Conference Record N2 - Volumetric representations of autoradiographic and histological images gain ever more interest as a base to interpret data obtained with /spl mu/-imaging devices like microPET. Beyond supporting spatial orientation within rat brains especially autoradiographic images may serve as a base to quantitatively evaluate the complex uptake patterns of microPET studies with receptor ligands or tumor tracers. They may also serve for the development of rat brain atlases or data models, which can be explored during further image analysis or simulation studies. In all cases a consistent spatial representation of the rat brain, i.e. its anatomy and the corresponding quantitative uptake pattern, is required. This includes both, a restacking of the individual two-dimensional images and the exact registration of the respective volumes. We propose strategies how these volumes can be created in a consistent way and trying to limit the requirements on the circumstances during data acquisition, i.e. being independent from other sources like video imaging of the block face prior to cutting or high resolution micro-X-ray CT or micro MRI. KW - Neoplasms KW - Data models KW - Image analysis KW - Brain modeling KW - Analytical models KW - Anatomy KW - Data acquisition KW - High-resolution imaging KW - Image resolution KW - Computed tomography Y1 - 2004 U6 - https://doi.org/10.1109/NSSMIC.2004.1466754 VL - 6 SP - 4001 EP - 4003 ER - TY - JOUR A1 - Bauer, Dagmar A1 - Hamacher, Kurt A1 - Bröer, Stefan A1 - Pauleit, Dirk A1 - Palm, Christoph A1 - Zilles, Karl A1 - Coenen, Heinz H. A1 - Langen, Karl-Josef T1 - Preferred stereoselective brain uptake of D-serine BT - a modulator of glutamatergic neurotransmission JF - Nuclear Medicine and Biology N2 - 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. KW - Aminosäuren KW - Gehirn KW - Blut-Hirn-Schranke KW - Aufnahme KW - d/l-serine KW - Amino acid transport KW - Blood–brain barrier KW - NMDA receptors Y1 - 2005 U6 - https://doi.org/10.1016/j.nucmedbio.2005.07.004 VL - 32 IS - 8 SP - 793 EP - 797 ER - TY - JOUR A1 - Lehmann, Thomas M. A1 - Palm, Christoph T1 - Color Line Search for Illuminant Estimation in Real World Scenes JF - Journal of the Optical Society of America (JOSA) A N2 - The estimation of illuminant color is mandatory for many applications in the field of color image quantification. However, it is an unresolved problem if no additional heuristics or restrictive assumptions apply. Assuming uniformly colored and roundly shaped objects, Lee has presented a theory and a method for computing the scene-illuminant chromaticity from specular highlights [H. C. Lee, J. Opt. Soc. Am. A 3, 1694 (1986)]. However, Lee’s method, called image path search, is less robust to noise and is limited in the handling of microtextured surfaces. We introduce a novel approach to estimate the color of a single illuminant for noisy and microtextured images, which frequently occur in real-world scenes. Using dichromatic regions of different colored surfaces, our approach, named color line search, reverses Lee’s strategy of image path search. Reliable color lines are determined directly in the domain of the color diagrams by three steps. First, regions of interest are automatically detected around specular highlights, and local color diagrams are computed. Second, color lines are determined according to the dichromatic reflection model by Hough transform of the color diagrams. Third, a consistency check is applied by a corresponding path search in the image domain. Our method is evaluated on 40 natural images of fruit and vegetables. In comparison with those of Lee’s method, accuracy and stability are substantially improved. In addition, the color line search approach can easily be extended to scenes of objects with macrotextured surfaces. Y1 - 2001 U6 - https://doi.org/10.1364/JOSAA.18.002679 VL - 18 IS - 11 SP - 2679 EP - 2691 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 - CHAP A1 - Palm, Christoph A1 - Vieten, Andrea A1 - Bauer, Dagmar A1 - Pietrzyk, Uwe T1 - Evaluierung von Registrierungsstrategien zur multimodalen 3D-Rekonstruktion von Rattenhirnschnitten T2 - Bildverarbeitung für die Medizin 2006 N2 - 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. KW - Registrierung KW - Dreidimensionale Rekonstruktion KW - Gehirn KW - Schnittpräparat Y1 - 2006 U6 - https://doi.org/10.1007/3-540-32137-3_51 SP - 251 EP - 255 PB - Springer CY - Berlin ER - TY - CHAP A1 - Pietrzyk, Uwe A1 - Palm, Christoph A1 - Beyer, Thomas T1 - Fusion strategies in multi-modality imaging T2 - Medical Physics, Vol 2. Proceedings of the jointly held Congresses: ICMP 2005, 14th International Conference of Medical Physics of the International Organization for Medical Physics (IOMP), the European Federation of Organizations in Medical Physics (EFOMP) and the German Society of Medical Physics (DGMP) ; BMT 2005, 39th Annual Congress of the German Society for Biomedical Engineering (DGBMT) within VDE ; 14th - 17th September 2005, Nuremberg, Germany KW - Bildgebendes Verfahren KW - Registrierung Y1 - 2005 SP - 1446 EP - 1447 ER - TY - GEN A1 - Weigert, Markus A1 - Palm, Christoph A1 - Quick, Harald H. A1 - Müller, Stefan P. A1 - Pietrzyk, Uwe A1 - Beyer, Thomas T1 - Template for MR-based attenuation correction for whole-body PET/MR imaging T2 - Nuklearmedizin KW - Kernspintomografie KW - Positronen-Emissions-Tomografie KW - Bildgebendes Verfahren KW - Schwächung Y1 - 2007 VL - 46 IS - 2 SP - A115 ER - TY - GEN A1 - Axer, Markus A1 - Axer, Hubertus A1 - Palm, Christoph A1 - Gräßel, David A1 - Zilles, Karl A1 - Pietrzyk, Uwe T1 - Visualization of Nerve Fibre Orientation in the Visual Cortex of the Human Brain by Means of Polarized Light T2 - Biomedizinische Technik KW - Sehrinde KW - Nervenfaser KW - Ausrichtung KW - Visualisierung KW - Polarisiertes Licht Y1 - 2007 VL - 52 IS - Suppl. SP - 1569048-041 ER - TY - CHAP A1 - Palm, Christoph A1 - Dehnhardt, Markus A1 - Vieten, Andrea A1 - Pietrzyk, Uwe T1 - 3D rat brain tumor reconstruction T2 - Biomedizinische Technik KW - Dreidimensionale Rekonstruktion KW - Hirntumor Y1 - 2005 VL - 50 IS - Suppl. 1, Part 1 SP - 597 EP - 598 ER - TY - GEN A1 - Palm, Christoph A1 - Crum, William R. A1 - Pietrzyk, Uwe A1 - Hawkes, David J. T1 - Application of Fluid and Elastic Registration Methods to Histological Rat Brain Sections T2 - Biomedizinische Technik KW - Registrierung KW - Gehirn KW - Schnittdarstellung Y1 - 2007 VL - 52 IS - Suppl. ER - TY - JOUR A1 - Brown, Peter A1 - Consortium, RELISH A1 - Zhou, Yaoqi A1 - Palm, Christoph T1 - Large expert-curated database for benchmarking document similarity detection in biomedical literature search JF - Database N2 - 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. KW - Information Retrieval KW - Indexierung KW - Literaturdatenbank KW - Dokument KW - Ähnlichkeitssuche KW - Suchmaschine Y1 - 2019 U6 - https://doi.org/10.1093/database/baz085 VL - 2019 SP - 1 EP - 66 PB - Oxford University Pres ER - TY - GEN A1 - Ebigbo, Alanna A1 - Mendel, Robert A1 - Probst, Andreas A1 - Manzeneder, Johannes A1 - Souza Jr., Luis Antonio de A1 - Papa, João Paulo A1 - Palm, Christoph A1 - Messmann, Helmut T1 - Artificial Intelligence in Early Barrett's Cancer: The Segmentation Task T2 - Endoscopy N2 - Aims: The delineation of outer margins of early Barrett's cancer can be challenging even for experienced endoscopists. Artificial intelligence (AI) could assist endoscopists faced with this task. As of date, there is very limited experience in this domain. In this study, we demonstrate the measure of overlap (Dice coefficient = D) between highly experienced Barrett endoscopists and an AI system in the delineation of cancer margins (segmentation task). Methods: An AI system with a deep convolutional neural network (CNN) was trained and tested on high-definition endoscopic images of early Barrett's cancer (n = 33) and normal Barrett's mucosa (n = 41). The reference standard for the segmentation task were the manual delineations of tumor margins by three highly experienced Barrett endoscopists. Training of the AI system included patch generation, patch augmentation and adjustment of the CNN weights. Then, the segmentation results from patch classification and thresholding of the class probabilities. Segmentation results were evaluated using the Dice coefficient (D). Results: The Dice coefficient (D) which can range between 0 (no overlap) and 1 (complete overlap) was computed only for images correctly classified by the AI-system as cancerous. At a threshold of t = 0.5, a mean value of D = 0.72 was computed. Conclusions: AI with CNN performed reasonably well in the segmentation of the tumor region in Barrett's cancer, at least when compared with expert Barrett's endoscopists. AI holds a lot of promise as a tool for better visualization of tumor margins but may need further improvement and enhancement especially in real-time settings. KW - Speiseröhrenkrankheit KW - Maschinelles Lernen KW - Barrett's esphagus KW - Deep Learning KW - Segmentation Y1 - 2019 U6 - https://doi.org/10.1055/s-0039-1681187 VL - 51 IS - 04 SP - 6 PB - Georg Thieme Verlag CY - Stuttgart ER - TY - CHAP A1 - Palm, Christoph A1 - Lehmann, Thomas M. A1 - Bredno, J. A1 - Neuschaefer-Rube, C. A1 - Klajman, S. A1 - Spitzer, Klaus T1 - Automated Analysis of Stroboscopic Image Sequences by Vibration Profiles T2 - Advances in Quantitative Laryngoscopy, Voice and Speech Research, Procs. 5th International Workshop N2 - A method for automated segmentation of vocal cords in stroboscopic video sequences is presented. In contrast to earlier approaches, the inner and outer contours of the vocal cords are independently delineated. Automatic segmentation of the low contrasted images is carried out by connecting the shape constraint of a point distribution model to a multi-channel regionbased balloon model. This enables us to robustly compute a vibration profile that is used as a new diagnostic tool to visualize several vibration parameters in only one graphic. The vibration profiles are studied in two cases: one physiological vibration and one functional pathology. KW - Vibration Profile KW - Stroboscopic Images KW - Contour Detection KW - Balloon Model KW - Point Distribution Model Y1 - 2001 UR - https://www.researchgate.net/publication/242439073_Automated_Analysis_of_Stroboscopic_Image_Sequences_by_Vibration_Profiles ER - TY - CHAP A1 - Palm, Christoph A1 - Fischer, B. A1 - Lehmann, Thomas M. A1 - Spitzer, Klaus T1 - Hierarchische Wasserscheiden-Transformation zur Lippensegmentierung in Farbbildern T2 - Bildverarbeitung für die Medizin 2000 N2 - 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%. KW - Hierarchische Wasserscheiden-Transformation KW - Segmentierung der Lippen KW - Bewegungsanalyse KW - Farbbildverarbeitung Y1 - 2000 U6 - https://doi.org/10.1007/978-3-642-59757-2_20 SP - 106 EP - 110 PB - Springer CY - Berlin ER - TY - CHAP A1 - Palm, Christoph A1 - Lehmann, Thomas M. A1 - Spitzer, Klaus T1 - Color Texture Analysis of Moving Vocal Cords Using Approaches from Statistics and Signal Theory T2 - Advances in Quantitative Laryngoscopy, Voice and Speech Research, Procs. 4th International Workshop, Friedrich Schiller University, Jena N2 - Textural features are applied for detection of morphological pathologies of vocal cords. Cooccurrence matrices as statistical features are presented as well as filter bank analysis by Gabor filters. Both methods are extended to handle color images. Their robustness against camera movement and vibration of vocal cords is evaluated. Classification results due to three in vivo sequences are in between 94.4 % and 98.9%. The classification errors decrease if color features are used instead of grayscale features for both statistical and Fourier features KW - Color Texture KW - Gabor Filter KW - Cooccurrence Matrix KW - Image Processing Y1 - 2000 SP - 49 EP - 56 ER - TY - JOUR A1 - Neuschaefer-Rube, C. A1 - Lehmann, Thomas M. A1 - Palm, Christoph A1 - Bredno, J. A1 - Klajman, S. A1 - Spitzer, Klaus T1 - 3D-Visualisierung glottaler Abduktionsbewegungen JF - Aktuelle phoniatrisch-pädaudiologische Aspekte Y1 - 2001 SN - 3-922766-76-5 VL - 2001/2002 IS - 9 SP - 58 EP - 61 PB - Median ER - TY - CHAP A1 - Fischer, B. A1 - Palm, Christoph A1 - Lehmann, Thomas M. A1 - Spitzer, Klaus T1 - Selektion von Farbtexturmerkmalen zur Tumorklassifikation dermatoskopischer Fotografien T2 - Bildverarbeitung für die Medizin 2002 Y1 - 2002 SP - 238 EP - 241 PB - Springer CY - Berlin ER - TY - BOOK A1 - Palm, Christoph T1 - Integrative Auswertung von Farbe und Textur Y1 - 2003 UR - http://publications.rwth-aachen.de/record/58707/files/Palm_Christoph.pdf PB - Der Andere Verlag ER - TY - JOUR A1 - Palm, Christoph A1 - Dehnhardt, Markus A1 - Vieten, Andrea A1 - Pietrzyk, Uwe A1 - Bauer, Andreas A1 - Zilles, Karl T1 - 3D rat brain tumors JF - Naunyn-Schmiedebergs Archives of Pharmacology Y1 - 2005 VL - 371 IS - R103 ER - 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 - CHAP A1 - Chang, Ching-Sheng A1 - Lin, Jin-Fa A1 - Lee, Ming-Ching A1 - Palm, Christoph ED - Tolxdorff, Thomas ED - Deserno, Thomas M. ED - Handels, Heinz ED - Maier, Andreas ED - Maier-Hein, Klaus H. ED - Palm, Christoph T1 - Semantic Lung Segmentation Using Convolutional Neural Networks T2 - Bildverarbeitung für die Medizin 2020. Algorithmen - Systeme - Anwendungen. Proceedings des Workshops vom 15. bis 17. März 2020 in Berlin N2 - 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. KW - Neuronales Netz KW - Segmentierung KW - Brustkorb KW - Deep Learning KW - Encoder-Decoder Network KW - Chest X-Ray Y1 - 2020 SN - 978-3-658-29266-9 U6 - https://doi.org/10.1007/978-3-658-29267-6_17 SP - 75 EP - 80 PB - Springer Vieweg CY - Wiesbaden ER - TY - CHAP A1 - Souza Jr., Luis Antonio de A1 - Ebigbo, Alanna A1 - Probst, Andreas A1 - Messmann, Helmut A1 - Papa, João Paulo A1 - Mendel, Robert A1 - Palm, Christoph T1 - Barrett's Esophagus Identification Using Color Co-occurrence Matrices T2 - 31st SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI), Parana, 2018 N2 - In this work, we propose the use of single channel Color Co-occurrence Matrices for texture description of Barrett’sEsophagus (BE)and adenocarcinoma images. Further classification using supervised learning techniques, such as Optimum-Path Forest (OPF), Support Vector Machines with Radial Basisunction (SVM-RBF) and Bayesian classifier supports the contextof automatic BE and adenocarcinoma diagnosis. We validated three approaches of classification based on patches, patients and images in two datasets (MICCAI 2015 and Augsburg) using the color-and-texture descriptors and the machine learning techniques. Concerning MICCAI 2015 dataset, the best results were obtained using the blue channel for the descriptors and the supervised OPF for classification purposes in the patch-based approach, with sensitivity nearly to 73% for positive adenocarcinoma identification and specificity close to 77% for BE (non-cancerous) patch classification. Regarding the Augsburg dataset, the most accurate results were also obtained using both OPF classifier and blue channel descriptor for the feature extraction, with sensitivity close to 67% and specificity around to76%. Our work highlights new advances in the related research area and provides a promising technique that combines color and texture information, allied to three different approaches of dataset pre-processing aiming to configure robust scenarios for the classification step. KW - Barrett’s Esophagus KW - Co-occurrence Matrices KW - Machine learning KW - Texture Analysis Y1 - 2018 U6 - https://doi.org/10.1109/SIBGRAPI.2018.00028 SP - 166 EP - 173 ER - TY - CHAP A1 - Middel, Luise A1 - Palm, Christoph A1 - Erdt, Marius T1 - Synthesis of Medical Images Using GANs T2 - Uncertainty for safe utilization of machine learning in medical imaging and clinical image-based procedures. First International Workshop, UNSURE 2019, and 8th International Workshop, CLIP 2019, held in conjunction with MICCAI 2019, Shenzhen, China, October 17, 2019 N2 - 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. KW - Neuronale Netze KW - Deep Learning KW - Generative adversarial networks KW - Machine Learning KW - Artificial Intelligence KW - Data privacy KW - Deep Learning KW - Bilderzeugung KW - Datenschutz Y1 - 2019 SN - 978-3-030-32688-3 U6 - https://doi.org/10.1007/978-3-030-32689-0_13 SN - 0302-9743 SP - 125 EP - 134 PB - Springer Nature CY - Cham ER - TY - CHAP A1 - Palm, Christoph A1 - Lehmann, Thomas M. A1 - Spitzer, Klaus T1 - Bestimmung der Lichtquellenfarbe bei der Endoskopie mikrotexturierter Oberflächen des Kehlkopfes T2 - 5. Workshop Farbbildverarbeitung, Ilmenau, 1999 N2 - Zur Unterstützung der Diagnose von Stimmlippenerkrankungen werden innerhalb des Forschungsprojektes Quantitative Digitale Laryngoskopie objektive Parameter zur Beschreibung der Bewegung, der Farbe sowie der Form der Stimmlippen entwickelt und klinisch evaluiert. Während die Bewegungsanalyse Aufschluß über funktionelle Stimmstörungen gibt, beschreiben Parameter der Farb- und Formanalyse morphologische Veränderungen des Stimmlippengewebes. In diesem Beitrag werden die Methoden und bisherigen Ergebnisse zur Bewegungs- und Farbanalyse vorgestellt. Die Bewegungsanalyse wurde mit einem erweiterten Konturmodell (Snakes) durchgeführt. Aufgrund des modifizierten Konturmodells konnten die Konturen der Stimmlippen automatisch über die gesmate Bildsequenz zuverlässig detektiert werden. Die Vermssung der Konturen liefert neue quantitative Parameter zur Befundung von laryngoskopischen Stimmlippenaufnahmen. Um die Farbeigenschaften der Stimmlippen zu bestimmen, wurde ausgehend vom RGB-Bild die Objektfarbe unabhängig von der Farbe der Lichtquelle durch Verwendung von Clusterverfahren und der Viertelkreisanalyse berechnet. Mit dieser Farbanalyse konnte die Farbe der Lichtquelle ermittelt und das beleuchtungsunabhängige Farbbild berechnet werden. Die Quanitifizierung der Rötung der Stimmlippen ist z.B. ein entscheidendes Kriterium zur Diagnostik der akuten Laryngitis. KW - Konturverfolgung KW - Snakes KW - Dichromatisches Reflexionsmodell KW - Farbkonstanz KW - Laryngoskopie Y1 - 1999 UR - http://www.germancolorgroup.de/html/Vortr_99_pdf/01_Palm.pdf SP - 3 EP - 10 ER - TY - CHAP A1 - Weiherer, Maximilian A1 - Zorn, Martin A1 - Wittenberg, Thomas A1 - Palm, Christoph ED - Tolxdorff, Thomas ED - Deserno, Thomas M. ED - Handels, Heinz ED - Maier, Andreas ED - Maier-Hein, Klaus H. ED - Palm, Christoph T1 - Retrospective Color Shading Correction for Endoscopic Images T2 - Bildverarbeitung für die Medizin 2020. Algorithmen - Systeme - Anwendungen. Proceedings des Workshops vom 15. bis 17. März 2020 in Berlin N2 - 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. KW - Endoskopie KW - Bildgebendes Verfahren KW - Farbenraum KW - Graustufe Y1 - 2020 SN - 978-3-658-29266-9 U6 - https://doi.org/10.1007/978-3-658-29267-6 SP - 14 EP - 19 PB - Springer Vieweg CY - Wiesbaden ER - TY - CHAP A1 - Palm, Christoph A1 - Keysers, Daniel A1 - Lehmann, Thomas M. A1 - Spitzer, Klaus T1 - Gabor Filtering of Complex Hue/Saturation Images for Color Texture Classification T2 - Proceedings of the 5th Joint Conference on Information Science (JCIS) 2, The Association for Intelligent Machinery, Atlantic City, NJ, 2000 N2 - 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. Y1 - 2000 UR - http://www.keysers.net/daniel/files/JCIS2000_palm.pdf SP - 45 EP - 49 ER - TY - JOUR A1 - Ebigbo, Alanna A1 - Mendel, Robert A1 - Probst, Andreas A1 - Manzeneder, Johannes A1 - Prinz, Friederike A1 - Souza Jr., Luis Antonio de A1 - Papa, João Paulo A1 - Palm, Christoph A1 - Messmann, Helmut T1 - Real-time use of artificial intelligence in the evaluation of cancer in Barrett’s oesophagus JF - Gut N2 - 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. KW - Speiseröhrenkrankheit KW - Diagnose KW - Maschinelles Lernen KW - Barrett's esophagus KW - Deep learning KW - real-time Y1 - 2020 U6 - https://doi.org/10.1136/gutjnl-2019-319460 VL - 69 IS - 4 SP - 615 EP - 616 PB - BMJ CY - London ER - TY - JOUR A1 - Palm, Christoph A1 - Lehmann, Thomas M. T1 - Classification of Color Textures by Gabor Filtering JF - Machine GRAPHICS & VISION Y1 - 2002 VL - 11 IS - 2/3 SP - 195 EP - 219 ER - TY - JOUR A1 - Arribas, Julia A1 - Antonelli, Giulio A1 - Frazzoni, Leonardo A1 - Fuccio, Lorenzo A1 - Ebigbo, Alanna A1 - van der Sommen, Fons A1 - Ghatwary, Noha A1 - Palm, Christoph A1 - Coimbra, Miguel A1 - Renna, Francesco A1 - Bergman, Jacques J.G.H.M. A1 - Sharma, Prateek A1 - Messmann, Helmut A1 - Hassan, Cesare A1 - Dinis-Ribeiro, Mario J. T1 - Standalone performance of artificial intelligence for upper GI neoplasia: a meta-analysis JF - Gut N2 - 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. KW - Artificial Intelligence Y1 - 2021 U6 - https://doi.org/10.1136/gutjnl-2020-321922 VL - 70 IS - 8 SP - 1458 EP - 1468 PB - BMJ CY - London ER - TY - JOUR A1 - Ebigbo, Alanna A1 - Mendel, Robert A1 - Rückert, Tobias A1 - Schuster, Laurin A1 - Probst, Andreas A1 - Manzeneder, Johannes A1 - Prinz, Friederike A1 - Mende, Matthias A1 - Steinbrück, Ingo A1 - Faiss, Siegbert A1 - Rauber, David A1 - Souza Jr., Luis Antonio de A1 - Papa, João Paulo A1 - Deprez, Pierre A1 - Oyama, Tsuneo A1 - Takahashi, Akiko A1 - Seewald, Stefan A1 - Sharma, Prateek A1 - Byrne, Michael F. A1 - Palm, Christoph A1 - Messmann, Helmut T1 - Endoscopic prediction of submucosal invasion in Barrett’s cancer with the use of Artificial Intelligence: A pilot Study JF - Endoscopy N2 - 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. KW - Maschinelles Lernen KW - Neuronales Netz KW - Speiseröhrenkrebs KW - Diagnose KW - Artificial Intelligence KW - Machine learning KW - Adenocarcinoma KW - Barrett’s cancer KW - submucosal invasion Y1 - 2021 U6 - https://doi.org/10.1055/a-1311-8570 VL - 53 IS - 09 SP - 878 EP - 883 PB - Thieme CY - Stuttgart ER - TY - JOUR A1 - Souza Jr., Luis Antonio de A1 - Passos, Leandro A. A1 - Mendel, Robert A1 - Ebigbo, Alanna A1 - Probst, Andreas A1 - Messmann, Helmut A1 - Palm, Christoph A1 - Papa, João Paulo T1 - Assisting Barrett's esophagus identification using endoscopic data augmentation based on Generative Adversarial Networks JF - Computers in Biology and Medicine N2 - Barrett's esophagus figured a swift rise in the number of cases in the past years. Although traditional diagnosis methods offered a vital role in early-stage treatment, they are generally time- and resource-consuming. In this context, computer-aided approaches for automatic diagnosis emerged in the literature since early detection is intrinsically related to remission probabilities. However, they still suffer from drawbacks because of the lack of available data for machine learning purposes, thus implying reduced recognition rates. This work introduces Generative Adversarial Networks to generate high-quality endoscopic images, thereby identifying Barrett's esophagus and adenocarcinoma more precisely. Further, Convolution Neural Networks are used for feature extraction and classification purposes. The proposed approach is validated over two datasets of endoscopic images, with the experiments conducted over the full and patch-split images. The application of Deep Convolutional Generative Adversarial Networks for the data augmentation step and LeNet-5 and AlexNet for the classification step allowed us to validate the proposed methodology over an extensive set of datasets (based on original and augmented sets), reaching results of 90% of accuracy for the patch-based approach and 85% for the image-based approach. Both results are based on augmented datasets and are statistically different from the ones obtained in the original datasets of the same kind. Moreover, the impact of data augmentation was evaluated in the context of image description and classification, and the results obtained using synthetic images outperformed the ones over the original datasets, as well as other recent approaches from the literature. Such results suggest promising insights related to the importance of proper data for the accurate classification concerning computer-assisted Barrett's esophagus and adenocarcinoma detection. KW - Maschinelles Lernen KW - Barrett's esophagus KW - Machine learning KW - Adenocarcinoma KW - Generative adversarial networks KW - Neuronales Netz KW - Adenocarcinom KW - Speiseröhrenkrebs KW - Diagnose Y1 - 2020 U6 - https://doi.org/10.1016/j.compbiomed.2020.104029 VL - 126 IS - November PB - Elsevier ER - TY - INPR A1 - Weiherer, Maximilian A1 - Eigenberger, Andreas A1 - Brébant, Vanessa A1 - Prantl, Lukas A1 - Palm, Christoph T1 - Learning the shape of female breasts: an open-access 3D statistical shape model of the female breast built from 110 breast scans N2 - 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. KW - Statistical shape mode KW - Surgical outcome simulation KW - 3D breast scan registration KW - Non-rigid surface registration KW - Breast imaging Y1 - 2021 ER - TY - CHAP A1 - Palm, Christoph A1 - Metzler, V. A1 - Moham, B. A1 - Dieker, O. A1 - Lehmann, Thomas M. A1 - Spitzer, Klaus ED - Evers, H. ED - Glombitza, G. ED - Lehmann, Thomas M. ED - Meinzer, H.-P. T1 - Co-Occurrence Matrizen zur Texturklassifikation in Vektorbildern T2 - Bildverarbeitung für die Medizin N2 - Statistische Eigenschaften natürlicher Grauwerttexturen werden mit Co-Occurrence Matrizen, basierend auf der Grauwertstatistik zweiter Ordnung, modelliert. Die Matrix gibt dann die apriori Wahrscheinlichkeiten aller Grauwertpaare an. Da in der medizinischen Bildverarbeitung verstärkt Multispektralbilder ausgewertet werden, wird das bekannte Konzept hier auf beliebige Vektorbilder erweitert. Dadurch kann bei der Texturklassifikation die zur Verfügung stehende Information vollständig genutzt werden. Insbesondere zur Detektion von Farbtexturen ist dieser Ansatz geeignet, da Wertepaare unterschiedlicher Spektralebenen ausgewertet werden können. Ebenso kann die Methode auch bei der Multiskalendekomposition von Intensitätsbildern zur Verbesserung der Texturerkennung beitragen. Die in den Matrizen entstehenden Muster lassen dann über die Extraktion geeigneter Texturdeskriptoren Rückschlüsse auf die Texturen des Bildes zu. KW - Texturerkennung KW - Vektorbilder KW - Multispektralbilder KW - Multiskalenbilder KW - Klassifikation Y1 - 1999 U6 - https://doi.org/10.1007/978-3-642-60125-5_69 SP - 367 EP - 371 PB - Springer CY - Berlin ER - TY - CHAP A1 - Palm, Christoph A1 - Pelkmann, Annegret A1 - Lehmann, Thomas M. A1 - Spitzer, Klaus T1 - Distortion Correction of Laryngoscopic Images T2 - Advances in quantitative laryngoscopy, voice and speech research, Proceedings of the 3rd international workshop Aachen, RWTH N2 - 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. KW - image distortion KW - camera calibration KW - multiple regression analysis Y1 - 1998 UR - https://pdfs.semanticscholar.org/e9d8/eb27af24bd79f482821441c2bf0eee7b3fe6.pdf?_ga=2.183754286.985176231.1591560247-1467258391.1581026068 SP - 117 EP - 125 ER - TY - CHAP A1 - Palm, Christoph A1 - Neuschaefer-Rube, C. A1 - Lehmann, Thomas M. A1 - Spitzer, Klaus ED - Evers, H. ED - Glombitza, G. ED - Lehmann, Thomas M. ED - Meinzer, H.-P. T1 - Wissensbasierte Bewegungskompensation in aktiven Konturmodellen T2 - Bildverarbeitung für die Medizin N2 - 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. KW - Aktives Konturmodell KW - Pradikatenlogik KW - Bewegungsschatzung Y1 - 1999 U6 - https://doi.org/10.1007/978-3-642-60125-5_2 SP - 8 EP - 12 PB - Springer CY - Berlin ER - TY - CHAP A1 - Palm, Christoph A1 - Scholl, Ingrid A1 - Lehmann, Thomas M. A1 - Spitzer, Klaus ED - Lehmann, Thomas M. ED - Metzler, V. ED - Spitzer, Klaus ED - Tolxdorff, Thomas T1 - Quantitative Farbmessung in laryngoskopischen Bildern T2 - Bildverarbeitung für die Medizin N2 - 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. KW - Farbkonstanz KW - quantitative Farbmessung KW - dichromatisches Reflexionsmodell KW - Laryngoskopie Y1 - 1998 U6 - https://doi.org/10.1007/978-3-642-58775-7_81 SP - 412 EP - 416 PB - Springer CY - Berlin ER - TY - JOUR A1 - Schulze, Elke A1 - Palm, Christoph A1 - Kerschbaum, Maximilian A1 - Seidel, Roman A1 - Lehmann, Lars A1 - Koller, Michael A1 - Pfingsten, Andrea T1 - KI-gestützte Untersuchung in der nicht-operativen Versorgung symptomgebender Erkrankungen des Kniegelenks – ein multiprofessionelles Konzept (KINEESIO) JF - MSK – Muskuloskelettale Physiotherapie N2 - Beschwerdebilder am Kniegelenk aufgrund muskuloskelettaler degenerativer oder verletzungsbedingter Erkrankungen sind häufig, nehmen im Alter zu und sind mit der steigenden Inanspruchnahme ärztlicher und therapeutischer Behandlungsmaßnahmen verbunden. Einer erfolgreichen Therapie gehen oft notwendige zeit- und ressourcenaufwendige Untersuchungen zur Erkennung und Differenzierung der patient*innenspezifischen Problematik voraus. Im Zusammenhang mit der nicht-operativen Versorgung des Kniegelenks hat ein sektorübergreifendes multiprofessionelles Forschungsteam ein Konzept entwickelt, um künstliche neuronale Netze so zu trainieren, dass sie bei der ärztlichen und physiotherapeutischen Untersuchung unterstützend Einsatz finden können. Denn gerade in der Erfassung und Auswertung umfassender Datenmengen liegen große Potenziale in der Künstlichen Intelligenz (KI) im Gesundheitswesen. Das Projekt KINEESIO trainiert und testet KI-gestützte Screening- Tools zur Untersuchung von Patient*innen mit Kniegelenkerkrankungen. Diese unterstützen die Abläufe zwischen Leistungserbringern und Patient*innen, tragen zu einer verbesserten Differenzierung individueller Beschwerdebilder bei und dienen Entscheidungsprozessen für eine adäquate Versorgung. Dadurch sollen Ressourcen im Gesundheitswesen geschont und eine qualitativ hochwertige Therapie ausreichend ermöglicht werden. N2 - Complaints regarding knee joints resulting from musculoskeletal degenerative or injury-related conditions are common, increase with age, and are associated with rising demand for treatments by physicians and therapists. The success of a therapeutic intervention is frequently contingent upon necessary time- and resource-intensive examinations to identify and differentiate the patient’s specific issues. In connection with non-surgical knee joint treatment, a crosssectoral, multidisciplinary research team has developed a concept for training artificial neural networks to assist in medical and physiotherapeutic examinations. After all, there is great potential for artificial intelligence (AI) in healthcare, particularly in collecting and analyzing large amounts of data. The KINEESIO project is training and testing screening tools supported by artificial intelligence for the assessment of patients with knee joint disorders. These tools support the workflows between healthcare providers and patients, improve the differentiation of individual symptoms and serve the decisionmaking process towards appropriate care. This should conserve healthcare resources and enable sufficient high-quality treatment. KW - Physiotherapie KW - Diagnose KW - Kniegelenk KW - Künstliche Intelligenz Y1 - 2024 U6 - https://doi.org/10.1055/a-2402-9982 SN - 2701-6986 VL - 28 IS - 5 SP - 312 EP - 321 PB - Thieme ER - TY - JOUR A1 - Hartmann, Robin A1 - Weiherer, Maximilian A1 - Schiltz, Daniel A1 - Seitz, Stephan A1 - Lotter, Luisa A1 - Anker, Alexandra A1 - Palm, Christoph A1 - Prantl, Lukas A1 - Brébant, Vanessa T1 - A Novel Method of Outcome Assessment in Breast Reconstruction Surgery: Comparison of Autologous and Alloplastic Techniques Using Three-Dimensional Surface Imaging JF - Aesthetic Plastic Surgery N2 - Background Breast reconstruction is an important coping tool for patients undergoing a mastectomy. There are numerous surgical techniques in breast reconstruction surgery (BRS). Regardless of the technique used, creating a symmetric outcome is crucial for patients and plastic surgeons. Three-dimensional surface imaging enables surgeons and patients to assess the outcome’s symmetry in BRS. To discriminate between autologous and alloplastic techniques, we analyzed both techniques using objective optical computerized symmetry analysis. Software was developed that enables clinicians to assess optical breast symmetry using three-dimensional surface imaging. Methods Twenty-seven patients who had undergone autologous (n = 12) or alloplastic (n = 15) BRS received three-dimensional surface imaging. Anthropomorphic data were collected digitally using semiautomatic measurements and automatic measurements. Automatic measurements were taken using the newly developed software. To quantify symmetry, a Symmetry Index is proposed. Results Statistical analysis revealed that there is no dif- ference in the outcome symmetry between the two groups (t test for independent samples; p = 0.48, two-tailed). Conclusion This study’s findings provide a foundation for qualitative symmetry assessment in BRS using automatized digital anthropometry. In the present trial, no difference in the outcomes’ optical symmetry was detected between autologous and alloplastic approaches. KW - Breast reconstruction KW - Breast symmetry KW - Digital anthropometry KW - Mammoplastik KW - Dreidimensionale Bildverarbeitung KW - Autogene Transplantation KW - Alloplastik Y1 - 2020 U6 - https://doi.org/10.1007/s00266-020-01749-4 VL - 44 SP - 1980 EP - 1987 PB - Springer CY - Heidelberg ER - TY - JOUR A1 - Hartmann, Robin A1 - Weiherer, Maximilian A1 - Schiltz, Daniel A1 - Baringer, Magnus A1 - Noisser, Vivien A1 - Hösl, Vanessa A1 - Eigenberger, Andreas A1 - Seitz, Stefan A1 - Palm, Christoph A1 - Prantl, Lukas A1 - Brébant, Vanessa T1 - New aspects in digital breast assessment: further refinement of a method for automated digital anthropometry JF - Archives of Gynecology and Obstetrics N2 - 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. KW - digital anthropometry KW - reconstructive surgery KW - 3D surface imaging Y1 - 2021 U6 - https://doi.org/10.1007/s00404-020-05862-2 SN - 1432-0711 VL - 303 SP - 721 EP - 728 PB - Springer Nature CY - Heidelberg ER - TY - CHAP A1 - Mendel, Robert A1 - Souza Jr., Luis Antonio de A1 - Rauber, David A1 - Papa, João Paulo A1 - Palm, Christoph T1 - Semi-supervised Segmentation Based on Error-Correcting Supervision T2 - Computer vision - ECCV 2020: 16th European conference, Glasgow, UK, August 23-28, 2020, Proceedings, Part XXIX N2 - 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. KW - Semi-Supervised Learning KW - Machine Learning Y1 - 2020 SN - 978-3-030-58525-9 U6 - https://doi.org/10.1007/978-3-030-58526-6_9 SP - 141 EP - 157 PB - Springer CY - Cham ER - TY - CHAP A1 - Palm, Christoph A1 - Scholl, Ingrid A1 - Lehmann, Thomas M. A1 - Spitzer, Klaus ED - Lehmann, Thomas M. ED - Scholl, Ingrid ED - Spitzer, Klaus T1 - Trennung von diffuser und spiegelnder Reflexion in Farbbildern des Larynx zur Untersuchung von Farb- und Formmerkmalen der Stimmlippen T2 - Bildverarbeitung für die Medizin. Algorithmen, Systeme, Anwendungen. Proceedings des Aachener Workshops am 8. und 9. November 1996 N2 - 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. KW - Laryngoskopie KW - Farbbild KW - Reflexion Y1 - 1996 UR - https://scholar.google.de/citations?user=nc0XkcMAAAAJ&hl=fa#d=gs_md_cita-d&u=%2Fcitations%3Fview_op%3Dview_citation%26hl%3Dfa%26user%3Dnc0XkcMAAAAJ%26citation_for_view%3Dnc0XkcMAAAAJ%3AqjMakFHDy7sC%26tzom%3D-120 SP - 229 EP - 234 PB - Verlag der Augustinus-Buchhandlung CY - Aachen ER -