@misc{BeyerWeigertPalmetal., author = {Beyer, Thomas and Weigert, Markus and Palm, Christoph and Quick, Harald H. and M{\"u}ller, Stefan P. and Pietrzyk, Uwe and Vogt, Florian and Martinez, M.J. and Bockisch, Andreas}, title = {Towards MR-based attenuation correction for whole-body PET/MR imaging}, series = {The Journal of Nuclear Medicine}, volume = {47}, journal = {The Journal of Nuclear Medicine}, number = {Suppl. 1}, pages = {384P}, subject = {Kernspintomografie}, language = {en} } @misc{GraesselAxerPalmetal., author = {Gr{\"a}ßel, David and Axer, Markus and Palm, Christoph and Dammers, J{\"u}rgen and Amunts, Katrin and Pietrzyk, Uwe and Zilles, Karl}, title = {Visualization of Fiber Tracts in the Postmortem Human Brain by Means of Polarized Light}, series = {NeuroImage}, volume = {47}, journal = {NeuroImage}, number = {Suppl. 1}, doi = {10.1016/S1053-8119(09)71415-6}, pages = {142}, subject = {Gehirn}, language = {en} } @misc{WeigertBeyerQuicketal., author = {Weigert, Markus and Beyer, Thomas and Quick, Harald H. and Pietrzyk, Uwe and Palm, Christoph and M{\"u}ller, Stefan P.}, title = {Generation of a MRI reference data set for the validation of automatic, non-rigid image co-registration algorithms}, series = {Nuklearmedizin}, volume = {46}, journal = {Nuklearmedizin}, number = {2}, pages = {A116}, subject = {Kernspintomografie}, language = {en} } @article{SouzaJrMendelStrasseretal., author = {Souza Jr., Luis Antonio de and Mendel, Robert and Strasser, Sophia and Ebigbo, Alanna and Probst, Andreas and Messmann, Helmut and Papa, Jo{\~a}o Paulo and Palm, Christoph}, title = {Convolutional Neural Networks for the evaluation of cancer in Barrett's esophagus: Explainable AI to lighten up the black-box}, series = {Computers in Biology and Medicine}, volume = {135}, journal = {Computers in Biology and Medicine}, publisher = {Elsevier}, issn = {0010-4825}, doi = {10.1016/j.compbiomed.2021.104578}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:898-opus4-20126}, pages = {1 -- 14}, abstract = {Even though artificial intelligence and machine learning have demonstrated remarkable performances in medical image computing, their level of accountability and transparency must be provided in such evaluations. The reliability related to machine learning predictions must be explained and interpreted, especially if diagnosis support is addressed. For this task, the black-box nature of deep learning techniques must be lightened up to transfer its promising results into clinical practice. Hence, we aim to investigate the use of explainable artificial intelligence techniques to quantitatively highlight discriminative regions during the classification of earlycancerous tissues in Barrett's esophagus-diagnosed patients. Four Convolutional Neural Network models (AlexNet, SqueezeNet, ResNet50, and VGG16) were analyzed using five different interpretation techniques (saliency, guided backpropagation, integrated gradients, input × gradients, and DeepLIFT) to compare their agreement with experts' previous annotations of cancerous tissue. We could show that saliency attributes match best with the manual experts' delineations. Moreover, there is moderate to high correlation between the sensitivity of a model and the human-and-computer agreement. The results also lightened that the higher the model's sensitivity, the stronger the correlation of human and computational segmentation agreement. We observed a relevant relation between computational learning and experts' insights, demonstrating how human knowledge may influence the correct computational learning.}, subject = {Deep Learning}, language = {en} } @inproceedings{PietrzykBauerVietenetal., author = {Pietrzyk, Uwe and Bauer, Dagmar and Vieten, Andrea and Bauer, Andreas and Langen, Karl-Josef and Zilles, Karl and Palm, Christoph}, title = {Creating consistent 3D multi-modality data sets from autoradiographic and histological images of the rat brain}, series = {IEEE Nuclear Science Symposium Conference Record}, volume = {6}, booktitle = {IEEE Nuclear Science Symposium Conference Record}, doi = {10.1109/NSSMIC.2004.1466754}, pages = {4001 -- 4003}, abstract = {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.}, language = {en} } @article{BauerHamacherBroeeretal., author = {Bauer, Dagmar and Hamacher, Kurt and Br{\"o}er, Stefan and Pauleit, Dirk and Palm, Christoph and Zilles, Karl and Coenen, Heinz H. and Langen, Karl-Josef}, title = {Preferred stereoselective brain uptake of D-serine}, series = {Nuclear Medicine and Biology}, volume = {32}, journal = {Nuclear Medicine and Biology}, number = {8}, doi = {10.1016/j.nucmedbio.2005.07.004}, pages = {793 -- 797}, abstract = {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.}, subject = {Aminos{\"a}uren}, language = {en} } @inproceedings{PalmVietenBaueretal., author = {Palm, Christoph and Vieten, Andrea and Bauer, Dagmar and Pietrzyk, Uwe}, title = {Evaluierung von Registrierungsstrategien zur multimodalen 3D-Rekonstruktion von Rattenhirnschnitten}, series = {Bildverarbeitung f{\"u}r die Medizin 2006}, booktitle = {Bildverarbeitung f{\"u}r die Medizin 2006}, publisher = {Springer}, address = {Berlin}, doi = {10.1007/3-540-32137-3_51}, pages = {251 -- 255}, abstract = {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 Modalit{\"a}t und der Konsistenz der Modalit{\"a}ten untereinander entwickelt, die auf bekannten Registrierungsmetriken basieren. Gerade bez{\"u}glich der Konsistenz der Modalit{\"a}ten untereinander zeigen zwei Strategien die besten Resultate: (1) abwechselnde multimodale Registrierung (2) monomodale Rekonstruktion einer Modalit{\"a}t und multimodale 2D Registrierung der zweiten Modalit{\"a}t.}, subject = {Registrierung }, language = {de} } @inproceedings{PietrzykPalmBeyer, author = {Pietrzyk, Uwe and Palm, Christoph and Beyer, Thomas}, title = {Fusion strategies in multi-modality imaging}, series = {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}, booktitle = {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}, pages = {1446 -- 1447}, subject = {Bildgebendes Verfahren}, language = {en} } @misc{WeigertPalmQuicketal., author = {Weigert, Markus and Palm, Christoph and Quick, Harald H. and M{\"u}ller, Stefan P. and Pietrzyk, Uwe and Beyer, Thomas}, title = {Template for MR-based attenuation correction for whole-body PET/MR imaging}, series = {Nuklearmedizin}, volume = {46}, journal = {Nuklearmedizin}, number = {2}, pages = {A115}, subject = {Kernspintomografie}, language = {en} } @misc{AxerAxerPalmetal., author = {Axer, Markus and Axer, Hubertus and Palm, Christoph and Gr{\"a}ßel, David and Zilles, Karl and Pietrzyk, Uwe}, title = {Visualization of Nerve Fibre Orientation in the Visual Cortex of the Human Brain by Means of Polarized Light}, series = {Biomedizinische Technik}, volume = {52}, journal = {Biomedizinische Technik}, number = {Suppl.}, pages = {1569048-041}, subject = {Sehrinde}, language = {en} } @inproceedings{PalmDehnhardtVietenetal., author = {Palm, Christoph and Dehnhardt, Markus and Vieten, Andrea and Pietrzyk, Uwe}, title = {3D rat brain tumor reconstruction}, series = {Biomedizinische Technik}, volume = {50}, booktitle = {Biomedizinische Technik}, number = {Suppl. 1, Part 1}, pages = {597 -- 598}, subject = {Dreidimensionale Rekonstruktion}, language = {en} } @misc{PalmCrumPietrzyketal., author = {Palm, Christoph and Crum, William R. and Pietrzyk, Uwe and Hawkes, David J.}, title = {Application of Fluid and Elastic Registration Methods to Histological Rat Brain Sections}, series = {Biomedizinische Technik}, volume = {52}, journal = {Biomedizinische Technik}, number = {Suppl.}, pages = {1569048-859}, subject = {Registrierung }, language = {en} } @article{BrownConsortiumZhouetal., author = {Brown, Peter and Consortium, RELISH and Zhou, Yaoqi and Palm, Christoph}, title = {Large expert-curated database for benchmarking document similarity detection in biomedical literature search}, series = {Database}, volume = {2019}, journal = {Database}, publisher = {Oxford University Pres}, doi = {10.1093/database/baz085}, pages = {1 -- 66}, abstract = {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.}, subject = {Information Retrieval}, language = {en} } @misc{EbigboMendelProbstetal., author = {Ebigbo, Alanna and Mendel, Robert and Probst, Andreas and Manzeneder, Johannes and Souza Jr., Luis Antonio de and Papa, Jo{\~a}o Paulo and Palm, Christoph and Messmann, Helmut}, title = {Artificial Intelligence in Early Barrett's Cancer: The Segmentation Task}, series = {Endoscopy}, volume = {51}, journal = {Endoscopy}, number = {04}, publisher = {Georg Thieme Verlag}, address = {Stuttgart}, doi = {10.1055/s-0039-1681187}, pages = {6}, abstract = {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.}, subject = {Speiser{\"o}hrenkrankheit}, language = {en} } @inproceedings{PalmFischerLehmannetal., author = {Palm, Christoph and Fischer, B. and Lehmann, Thomas M. and Spitzer, Klaus}, title = {Hierarchische Wasserscheiden-Transformation zur Lippensegmentierung in Farbbildern}, series = {Bildverarbeitung f{\"u}r die Medizin 2000}, booktitle = {Bildverarbeitung f{\"u}r die Medizin 2000}, publisher = {Springer}, address = {Berlin}, doi = {10.1007/978-3-642-59757-2_20}, pages = {106 -- 110}, abstract = {Zur L{\"o}sung komplexer Segmentierungsprobleme wird eine hierarchische und farbbasierte Wasserscheidentransformation vorgestellt. Geringe Modifikationen bez{\"u}glich Startpunktwahl und Flutungsprozess resultieren in signifikanten Verbesserungen der Segmentierung. Das Verfahren wurde zur Lippendetektion in Farbbildsequenzen eingesetzt, die zur quantitativen Beschreibung von Sprechbewegungsabl{\"a}ufen automatisch ausgewertet werden. Die Experimente mit 245 Bildern aus 6 Sequenzen zeigten eine Fehlerrate von 13\%.}, language = {de} } @article{NeuschaeferRubeLehmannPalmetal., author = {Neuschaefer-Rube, C. and Lehmann, Thomas M. and Palm, Christoph and Bredno, J. and Klajman, S. and Spitzer, Klaus}, title = {3D-Visualisierung glottaler Abduktionsbewegungen}, series = {Aktuelle phoniatrisch-p{\"a}daudiologische Aspekte}, volume = {2001/2002}, journal = {Aktuelle phoniatrisch-p{\"a}daudiologische Aspekte}, number = {9}, publisher = {Median}, isbn = {3-922766-76-5}, pages = {58 -- 61}, language = {de} } @inproceedings{FischerPalmLehmannetal., author = {Fischer, B. and Palm, Christoph and Lehmann, Thomas M. and Spitzer, Klaus}, title = {Selektion von Farbtexturmerkmalen zur Tumorklassifikation dermatoskopischer Fotografien}, series = {Bildverarbeitung f{\"u}r die Medizin 2002}, booktitle = {Bildverarbeitung f{\"u}r die Medizin 2002}, publisher = {Springer}, address = {Berlin}, pages = {238 -- 241}, language = {de} } @article{PalmDehnhardtVietenetal., author = {Palm, Christoph and Dehnhardt, Markus and Vieten, Andrea and Pietrzyk, Uwe and Bauer, Andreas and Zilles, Karl}, title = {3D rat brain tumors}, series = {Naunyn-Schmiedebergs Archives of Pharmacology}, volume = {371}, journal = {Naunyn-Schmiedebergs Archives of Pharmacology}, number = {R103}, language = {en} } @inproceedings{PalmSchollLehmannetal., author = {Palm, Christoph and Scholl, Ingrid and Lehmann, Thomas M. and Spitzer, Klaus}, title = {Nutzung eines Farbkonstanz-Algorithmus zur Entfernung von Glanzlichtern in laryngoskopischen Bildern}, series = {Methoden der Medizinischen Informatik, Biometrie und Epidemiologie in der modernen Informationsgesellschaft}, booktitle = {Methoden der Medizinischen Informatik, Biometrie und Epidemiologie in der modernen Informationsgesellschaft}, editor = {Greiser, E. and Wischnewsky, M.}, publisher = {MMV Medien und Medizin}, address = {M{\"u}nchen}, isbn = {9783820813357}, pages = {300 -- 303}, abstract = {1 Einf{\"u}hrung Funktionelle und organische St{\"o}rungen im Larynx beeintr{\"a}chtigen die Ausdrucksf{\"a}higkeit 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 unabh{\"a}ngige Aufnahme der drei Farbkan{\"a}le erlaubt. Die bisherige subjektive Befundung ist von der Erfahrung des Untersuchers abh{\"a}ngig und l{\"a}ßt nur eine grobe Klassifikation der Krankheitsbilder zu. Zur Objektivierung werden daher quantitative Parameter f{\"u}r 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 f{\"u}hren und so weite Bereiche der Stimmlippen f{\"u}r die Farb-und Texturanalyse unbrauchbar machen. Dieser Beitrag stellt einen Farbkonstanz-Algorithmus vor, der unabh{\"a}ngig von der Lichtquelle quantitative Farbwerte des Gewebes liefert und die Glanzlichtdetektion und -elimination erm{\"o}glicht. 2 Methodik Ziel des Farbkonstanz-Algorithmus ist die Trennung von Lichtquellen-und Gewebefarbe. Unter Verwendung des dichromatischen Reflexionsmodells [1] kann die Oberfl{\"a}chenreflexion mit der Farbe der Lichtquelle und die K{\"o}rperreflexion mit der Gewebefarbe identifiziert werden. Der Farbeindruck entsteht aus der Linearkombination beider Farbkomponenten. Ihre Gewichtung ist von der Aufnahmegeometrie abh{\"a}ngig, insbesondere vom Winkel zwischen Oberfl{\"a}chennormalen und dem Positionsvektor der Lichtquelle. In einem zweistufigen Verfahren wird zun{\"a}chst die Lichtquellenfarbe gesch{\"a}tzt, dann die Gewebefarbe ermittelt. Hieraus k{\"o}nnen beide Farbanteile durch die Berechnung der Gewichtsfaktoren pixelweise getrennt werden.}, language = {de} } @inproceedings{ChangLinLeeetal., author = {Chang, Ching-Sheng and Lin, Jin-Fa and Lee, Ming-Ching and Palm, Christoph}, title = {Semantic Lung Segmentation Using Convolutional Neural Networks}, series = {Bildverarbeitung f{\"u}r die Medizin 2020. Algorithmen - Systeme - Anwendungen. Proceedings des Workshops vom 15. bis 17. M{\"a}rz 2020 in Berlin}, booktitle = {Bildverarbeitung f{\"u}r die Medizin 2020. Algorithmen - Systeme - Anwendungen. Proceedings des Workshops vom 15. bis 17. M{\"a}rz 2020 in Berlin}, editor = {Tolxdorff, Thomas and Deserno, Thomas M. and Handels, Heinz and Maier, Andreas and Maier-Hein, Klaus H. and Palm, Christoph}, publisher = {Springer Vieweg}, address = {Wiesbaden}, isbn = {978-3-658-29266-9}, doi = {10.1007/978-3-658-29267-6_17}, pages = {75 -- 80}, abstract = {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.}, subject = {Neuronales Netz}, language = {en} } @inproceedings{SouzaJrEbigboProbstetal., author = {Souza Jr., Luis Antonio de and Ebigbo, Alanna and Probst, Andreas and Messmann, Helmut and Papa, Jo{\~a}o Paulo and Mendel, Robert and Palm, Christoph}, title = {Barrett's Esophagus Identification Using Color Co-occurrence Matrices}, series = {31st SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI), Parana, 2018}, booktitle = {31st SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI), Parana, 2018}, doi = {10.1109/SIBGRAPI.2018.00028}, pages = {166 -- 173}, abstract = {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.}, language = {en} } @inproceedings{MiddelPalmErdt, author = {Middel, Luise and Palm, Christoph and Erdt, Marius}, title = {Synthesis of Medical Images Using GANs}, series = {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}, booktitle = {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}, publisher = {Springer Nature}, address = {Cham}, isbn = {978-3-030-32688-3}, issn = {0302-9743}, doi = {10.1007/978-3-030-32689-0_13}, pages = {125 -- 134}, abstract = {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.}, subject = {Neuronale Netze}, language = {en} } @inproceedings{WeihererZornWittenbergetal., author = {Weiherer, Maximilian and Zorn, Martin and Wittenberg, Thomas and Palm, Christoph}, title = {Retrospective Color Shading Correction for Endoscopic Images}, series = {Bildverarbeitung f{\"u}r die Medizin 2020. Algorithmen - Systeme - Anwendungen. Proceedings des Workshops vom 15. bis 17. M{\"a}rz 2020 in Berlin}, booktitle = {Bildverarbeitung f{\"u}r die Medizin 2020. Algorithmen - Systeme - Anwendungen. Proceedings des Workshops vom 15. bis 17. M{\"a}rz 2020 in Berlin}, editor = {Tolxdorff, Thomas and Deserno, Thomas M. and Handels, Heinz and Maier, Andreas and Maier-Hein, Klaus H. and Palm, Christoph}, publisher = {Springer Vieweg}, address = {Wiesbaden}, isbn = {978-3-658-29266-9}, doi = {10.1007/978-3-658-29267-6}, pages = {14 -- 19}, abstract = {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.}, subject = {Endoskopie}, language = {en} } @inproceedings{PalmKeysersLehmannetal., author = {Palm, Christoph and Keysers, Daniel and Lehmann, Thomas M. and Spitzer, Klaus}, title = {Gabor Filtering of Complex Hue/Saturation Images for Color Texture Classification}, series = {Proceedings of the 5th Joint Conference on Information Science (JCIS) 2, The Association for Intelligent Machinery, Atlantic City, NJ, 2000}, booktitle = {Proceedings of the 5th Joint Conference on Information Science (JCIS) 2, The Association for Intelligent Machinery, Atlantic City, NJ, 2000}, pages = {45 -- 49}, abstract = {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.}, language = {en} }