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 - Mendel, Robert A1 - Rauber, David A1 - Souza Jr., Luis Antonio de A1 - Papa, João Paulo A1 - Palm, Christoph T1 - Error-Correcting Mean-Teacher: Corrections instead of consistency-targets applied to semi-supervised medical image segmentation JF - Computers in Biology and Medicine N2 - Semantic segmentation is an essential task in medical imaging research. Many powerful deep-learning-based approaches can be employed for this problem, but they are dependent on the availability of an expansive labeled dataset. In this work, we augment such supervised segmentation models to be suitable for learning from unlabeled data. Our semi-supervised approach, termed Error-Correcting Mean-Teacher, uses an exponential moving average model like the original Mean Teacher but introduces our new paradigm of error correction. The original segmentation network is augmented to handle this secondary correction task. Both tasks build upon the core feature extraction layers of the model. For the correction task, features detected in the input image are fused with features detected in the predicted segmentation and further processed with task-specific decoder layers. The combination of image and segmentation features allows the model to correct present mistakes in the given input pair. The correction task is trained jointly on the labeled data. On unlabeled data, the exponential moving average of the original network corrects the student’s prediction. The combined outputs of the students’ prediction with the teachers’ correction form the basis for the semi-supervised update. We evaluate our method with the 2017 and 2018 Robotic Scene Segmentation data, the ISIC 2017 and the BraTS 2020 Challenges, a proprietary Endoscopic Submucosal Dissection dataset, Cityscapes, and Pascal VOC 2012. Additionally, we analyze the impact of the individual components and examine the behavior when the amount of labeled data varies, with experiments performed on two distinct segmentation architectures. Our method shows improvements in terms of the mean Intersection over Union over the supervised baseline and competing methods. Code is available at https://github.com/CloneRob/ECMT. KW - Semi-supervised Segmentation KW - Mean-Teacher KW - Pseudo-labels KW - Medical Imaging Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:898-opus4-57790 SN - 0010-4825 N1 - Corresponding author der OTH Regensburg: Robert Mendel VL - 154 IS - March PB - Elsevier ER - TY - JOUR A1 - Maier, Andreas A1 - Deserno, Thomas M. A1 - Handels, Heinz A1 - Maier-Hein, Klaus H. A1 - Palm, Christoph A1 - Tolxdorff, Thomas T1 - IJCARS: BVM 2021 special issue JF - International Journal of Computer Assisted Radiology and Surgery N2 - The German workshop on medical image computing (BVM) has been held in different locations in Germany for more than 20 years. In terms of content, BVM focused on the computer-aided analysis of medical image data with a wide range of applications, e.g. in the area of imaging, diagnostics, operation planning, computer-aided intervention and visualization. During this time, there have been remarkable methodological developments and upheavals, on which the BVM community has worked intensively. The area of machine learning should be emphasized, which has led to significant improvements, especially for tasks of classification and segmentation, but increasingly also in image formation and registration. As a result, work in connection with deep learning now dominates the BVM. These developments have also contributed to the establishment of medical image processing at the interface between computer science and medicine as one of the key technologies for the digitization of the health system. In addition to the presentation of current research results, a central aspect of the BVM is primarily the promotion of young scientists from the diverse BVM community, covering not only Germany but also Austria, Switzerland, The Netherland and other European neighbors. The conference serves primarily doctoral students and postdocs, but also students with excellent bachelor and master theses as a platform to present their work, to enter into professional discourse with the community, and to establish networks with specialist colleagues. Despite the many conferences and congresses that are also relevant for medical image processing, the BVM has therefore lost none of its importance and attractiveness and has retained its permanent place in the annual conference rhythm. Building on this foundation, there are some innovations and changes this year. The BVM 2021 was organized for the first time at the Ostbayerische Technische Hochschule Regensburg (OTH Regensburg, a technical university of applied sciences). After Aachen, Berlin, Erlangen, Freiburg, Hamburg, Heidelberg, Leipzig, Lübeck, and Munich, Regensburg is not just a new venue. OTH Regensburg is the first representative of the universities of applied sciences (HAW) to organize the conference, which differs to universities, university hospitals, or research centers like Fraunhofer or Helmholtz. This also considers the further development of the research landscape in Germany, where HAWs increasingly contribute to applied research in addition to their focus on teaching. This development is also reflected in the contributions submitted to the BVM in recent years. At BVM 2021, which was held in a virtual format for the first time due to the Corona pandemic, an attractive and high-quality program was offered. Fortunately, the number of submissions increased significantly. Out of 97 submissions, 26 presentations, 51 posters and 5 software demonstrations were accepted via an anonymized reviewing process with three reviews each. The three best works have been awarded BVM prizes, selected by a separate committee. Based on these high-quality submissions, we are able to present another special issue in the International Journal of Computer Assisted Radiology and Surgery (IJCARS). Out of the 97 submissions, the ones with the highest scores have been invited to submit an extended version of their paper to be presented in IJCARS. As a result, we are now able to present this special issue with seven excellent articles. Many submissions focus on machine learning in a medical context. KW - Medical Image Computing KW - Bildgebendes Verfahren KW - Medizin Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:898-opus4-21666 VL - 16 SP - 2067 EP - 2068 PB - Springer ER -