TY - GEN A1 - Meinikheim, Michael A1 - Mendel, Robert A1 - Probst, Andreas A1 - Scheppach, Markus W. A1 - Schnoy, Elisabeth A1 - Nagl, Sandra A1 - Römmele, Christoph A1 - Prinz, Friederike A1 - Schlottmann, Jakob A1 - Golger, Daniela A1 - Palm, Christoph A1 - Messmann, Helmut A1 - Ebigbo, Alanna T1 - AI-assisted detection and characterization of early Barrett's neoplasia: Results of an Interim analysis T2 - Endoscopy N2 - Aims  Evaluation of the add-on effect an artificial intelligence (AI) based clinical decision support system has on the performance of endoscopists with different degrees of expertise in the field of Barrett's esophagus (BE) and Barrett's esophagus-related neoplasia (BERN). Methods  The support system is based on a multi-task deep learning model trained to solve a segmentation and several classification tasks. The training approach represents an extension of the ECMT semi-supervised learning algorithm. The complete system evaluates a decision tree between estimated motion, classification, segmentation, and temporal constraints, to decide when and how the prediction is highlighted to the observer. In our current study, ninety-six video cases of patients with BE and BERN were prospectively collected and assessed by Barrett's specialists and non-specialists. All video cases were evaluated twice – with and without AI assistance. The order of appearance, either with or without AI support, was assigned randomly. Participants were asked to detect and characterize regions of dysplasia or early neoplasia within the video sequences. Results  Standalone sensitivity, specificity, and accuracy of the AI system were 92.16%, 68.89%, and 81.25%, respectively. Mean sensitivity, specificity, and accuracy of expert endoscopists without AI support were 83,33%, 58,20%, and 71,48 %, respectively. Gastroenterologists without Barrett's expertise but with AI support had a comparable performance with a mean sensitivity, specificity, and accuracy of 76,63%, 65,35%, and 71,36%, respectively. Conclusions  Non-Barrett's experts with AI support had a similar performance as experts in a video-based study. Y1 - 2023 U6 - https://doi.org/10.1055/s-0043-1765437 VL - 55 IS - S02 PB - Thieme ER - TY - JOUR A1 - Scheppach, Markus W. A1 - Mendel, Robert A1 - Muzalyova, Anna A1 - Rauber, David A1 - Probst, Andreas A1 - Nagl, Sandra A1 - Römmele, Christoph A1 - Yip, Hon Chi A1 - Lau, Louis Ho Shing A1 - Gölder, Stefan Karl A1 - Schmidt, Arthur A1 - Kouladouros, Konstantinos A1 - Abdelhafez, Mohamed A1 - Walter, Benjamin M. A1 - Meinikheim, Michael A1 - Chiu, Philip Wai Yan A1 - Palm, Christoph A1 - Messmann, Helmut A1 - Ebigbo, Alanna T1 - Artificial intelligence improves submucosal vessel detection during third space endoscopy JF - Endoscopy N2 - Background and study aims: While artificial intelligence (AI) shows high potential in decision support for diagnostic gastrointestinal endoscopy, its role in therapeutic endoscopy remains unclear. Third space endoscopic procedures pose the risk of intraprocedural bleeding. Therefore, we aimed to develop an AI algorithm for intraprocedural blood vessel detection. Patients and Methods: Using a test dataset with 101 standardized video clips containing 200 predefined submucosal blood vessels, 19 endoscopists were evaluated for the vessel detection rate (VDR) and time (VDT) with and without support of an AI algorithm. Test subjects were grouped according to experience in ESD. Results: With AI support, endoscopists VDR increased from 56.4% [CI 54.1–58.6] to 72.4% [CI 70.3–74.4]. Endoscopists‘ VDT dropped from 6.7sec [CI 6.2-7.1] to 5.2sec [CI 4.8-5.7]. False positive (FP) readings appeared in 4.5% of frames and were marked significantly shorter than true positives (6.0sec [CI 5.28-6.70] vs. 0.7sec [CI 0.55-0.87]). Conclusions: AI improved the vessel detection rate and time of endoscopists during third space endoscopy. While these data need to be corroborated by clinical trials, AI may prove to be an invaluable tool for the improvement of endoscopic interventions. KW - Artificial Intelligence KW - Third Space Endoscopy Y1 - 2025 U6 - https://doi.org/10.1055/a-2534-1164 PB - Thieme CY - Stuttgart ER - TY - JOUR A1 - Rückert, Tobias A1 - Rückert, Daniel A1 - Palm, Christoph T1 - Methods and datasets for segmentation of minimally invasive surgical instruments in endoscopic images and videos: A review of the state of the art JF - Computers in Biology and Medicine N2 - In the field of computer- and robot-assisted minimally invasive surgery, enormous progress has been made in recent years based on the recognition of surgical instruments in endoscopic images and videos. In particular, the determination of the position and type of instruments is of great interest. Current work involves both spatial and temporal information, with the idea that predicting the movement of surgical tools over time may improve the quality of the final segmentations. The provision of publicly available datasets has recently encouraged the development of new methods, mainly based on deep learning. In this review, we identify and characterize datasets used for method development and evaluation and quantify their frequency of use in the literature. We further present an overview of the current state of research regarding the segmentation and tracking of minimally invasive surgical instruments in endoscopic images and videos. The paper focuses on methods that work purely visually, without markers of any kind attached to the instruments, considering both single-frame semantic and instance segmentation approaches, as well as those that incorporate temporal information. The publications analyzed were identified through the platforms Google Scholar, Web of Science, and PubMed. The search terms used were “instrument segmentation”, “instrument tracking”, “surgical tool segmentation”, and “surgical tool tracking”, resulting in a total of 741 articles published between 01/2015 and 07/2023, of which 123 were included using systematic selection criteria. A discussion of the reviewed literature is provided, highlighting existing shortcomings and emphasizing the available potential for future developments. KW - Deep Learning KW - Minimal-invasive Chirurgie KW - Bildsegmentierung KW - Surgical instrument segmentation KW - Surgical instrument tracking KW - Spatio-temporal information KW - Endoscopic surgery KW - Robot-assisted surgery Y1 - 2024 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:898-opus4-69830 N1 - Corresponding author: Tobias Rückert N1 - Corrigendum unter: https://opus4.kobv.de/opus4-oth-regensburg/frontdoor/index/index/docId/7033 VL - 169 PB - Elsevier CY - Amsterdam ER - TY - INPR A1 - Allan, Max A1 - Kondo, Satoshi A1 - Bodenstedt, Sebastian A1 - Leger, Stefan A1 - Kadkhodamohammadi, Rahim A1 - Luengo, Imanol A1 - Fuentes, Felix A1 - Flouty, Evangello A1 - Mohammed, Ahmed A1 - Pedersen, Marius A1 - Kori, Avinash A1 - Alex, Varghese A1 - Krishnamurthi, Ganapathy A1 - Rauber, David A1 - Mendel, Robert A1 - Palm, Christoph A1 - Bano, Sophia A1 - Saibro, Guinther A1 - Shih, Chi-Sheng A1 - Chiang, Hsun-An A1 - Zhuang, Juntang A1 - Yang, Junlin A1 - Iglovikov, Vladimir A1 - Dobrenkii, Anton A1 - Reddiboina, Madhu A1 - Reddy, Anubhav A1 - Liu, Xingtong A1 - Gao, Cong A1 - Unberath, Mathias A1 - Kim, Myeonghyeon A1 - Kim, Chanho A1 - Kim, Chaewon A1 - Kim, Hyejin A1 - Lee, Gyeongmin A1 - Ullah, Ihsan A1 - Luna, Miguel A1 - Park, Sang Hyun A1 - Azizian, Mahdi A1 - Stoyanov, Danail A1 - Maier-Hein, Lena A1 - Speidel, Stefanie T1 - 2018 Robotic Scene Segmentation Challenge N2 - In 2015 we began a sub-challenge at the EndoVis workshop at MICCAI in Munich using endoscope images of exvivo tissue with automatically generated annotations from robot forward kinematics and instrument CAD models. However, the limited background variation and simple motion rendered the dataset uninformative in learning about which techniques would be suitable for segmentation in real surgery. In 2017, at the same workshop in Quebec we introduced the robotic instrument segmentation dataset with 10 teams participating in the challenge to perform binary, articulating parts and type segmentation of da Vinci instruments. This challenge included realistic instrument motion and more complex porcine tissue as background and was widely addressed with modfications on U-Nets and other popular CNN architectures [1]. In 2018 we added to the complexity by introducing a set of anatomical objects and medical devices to the segmented classes. To avoid over-complicating the challenge, we continued with porcine data which is dramatically simpler than human tissue due to the lack of fatty tissue occluding many organs. KW - Minimally invasive surgery KW - Robotic KW - Minimal-invasive Chirurgie KW - Robotik Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:898-opus4-50049 UR - https://arxiv.org/abs/2001.11190 ER - TY - GEN ED - Maier-Hein, Klaus H. ED - Deserno, Thomas M. ED - Handels, Heinz ED - Maier, Andreas ED - Palm, Christoph ED - Tolxdorff, Thomas T1 - Bildverarbeitung für die Medizin 2022 BT - Proceedings, German Workshop on Medical Image Computing, Heidelberg, June 26-28, 2022 T2 - Informatik aktuell N2 - Die Tagung Bildverarbeitung für die Medizin (BVM) wird seit weit mehr als 20 Jahren an wechselnden Orten Deutschlands veranstaltet. Inhaltlich fokussiert sich die BVM dabei auf die computergestützte Analyse medizinischer Bilddaten mit vielfältigen Anwendungsgebieten, z.B. im Bereich der Bildgebung, der Diagnostik, der Operationsplanung, der computerunterstützten Intervention und der Visualisierung. In dieser Zeit hat es bemerkenswerte methodische Weiterentwicklungen und Umbrüche gegeben, wie zum Beispiel im Bereich des maschinellen Lernens, an denen die BVM-Community intensiv mitgearbeitet hat. In der Folge dominieren inzwischen Arbeiten im Zusammenhang mit Deep Learning die BVM. Auch diese Entwicklungen haben dazu beigetragen, dass die Medizinische Bildverarbeitung an der Schnittstelle zwischen Informatik und Medizin als eine der Schlüsseltechnologien zur Digitalisierung des Gesundheitswesens etabliert ist. Zentraler Aspekt der BVM ist neben der Darstellung aktueller Forschungsergebnisse schwerpunktmäßig aus der vielfältigen deutschlandweiten BVM-Community insbesondere die Förderung des wissenschaftlichen Nachwuchses. Die Tagung dient vor allem Doktorand*innen und Postdoktorand*innen, aber auch Studierenden mit hervorragenden Bachelor- und Masterarbeiten als Plattform, um ihre Arbeiten zu präsentieren, dabei in den fachlichen Diskurs mit der Community zu treten und Netzwerke mit Fachkolleg*innen zu knüpfen. Trotz der vielen Tagungen und Kongresse, die auch für die Medizinische Bildverarbeitung relevant sind, hat die BVM deshalb nichts von ihrer Bedeutung und Anziehungskraft eingebüßt. Inhaltlich kann auch bei der BVM 2022 wieder ein attraktives und hochklassiges Programm geboten werden. Es wurden aus 88 Einreichungen über ein anonymisiertes Reviewing-Verfahren mit jeweils drei Reviews 24 Vorträge, 33 Posterbeiträge und eine Softwaredemonstration angenommen. Da aufgrund der strengen Covid Hygiene- und Abstandsregeln leider nur sehr wenige klassische Posterbeiträge zugelassen wurden, wird dieses Jahr erstmalig ein neues Format umgesetzt. Hierfür wurden 13 weitere Beiträge als e-Poster angenommen. Die besten Arbeiten werden auch in diesem Jahr mit Preisen ausgezeichnet. Die Webseite des Workshops findet sich unter https://www.bvm-workshop.org. Das Programm wird durch drei eingeladene Vorträge ergänzt: - Prof. Dr. Ullrich Köthe, Visual Learning Lab, Universität Heidelberg - Prof. Mihaela van der Schaar, University of Cambridge, UK - Prof. Dr. Stefanie Speidel, Translational Surgical Oncology, NCT Dresden Des Weiteren werden im Vorfeld der BVM drei Tutorials angeboten: - Known Operator Learning and Hybrid Machine Learning in Medical Imaging: The Past, the Present and the Future (FAU Erlangen-Nürnberg) - Advanced Deep Learning (DKFZ Heidelberg) - Hands-On Medical Image Registration (Universität zu Lübeck) An dieser Stelle möchten wir allen, die bei den umfangreichen Vorbereitungen zum Gelingen des Workshops beigetragen haben, unseren herzlichen Dank für ihr Engagement aussprechen: den Referent*innen der Gastvorträge, den Autor*innen der Beiträge, den Referent*innen der Tutorien, den Industrierepräsentant*innen, dem Programmkomitee, den Fachgesellschaften, den Mitgliedern des BVM-Organisationsteams und allen Mitarbeitenden der Abteilung Medical Image Computing des Deutschen Krebsforschungszentrums. Wir wünschen allen Teilnehmer*innen des Workshops BVM 2022 spannende neue Kontakte und inspirierende Eindrücke aus der Welt der medizinischen Bildverarbeitung. KW - Medical Image Computing KW - Machine Learning Y1 - 2022 SN - 978-3-658-36932-3 U6 - https://doi.org/10.1007/978-3-658-36932-3 PB - Springer Vieweg CY - Wiesbaden ER - TY - GEN A1 - Scheppach, Markus W. A1 - Rauber, David A1 - Mendel, Robert A1 - Palm, Christoph A1 - Byrne, Michael F. A1 - Messmann, Helmut A1 - Ebigbo, Alanna T1 - Detection Of Celiac Disease Using A Deep Learning Algorithm T2 - Endoscopy N2 - Aims Celiac disease (CD) is a complex condition caused by an autoimmune reaction to ingested gluten. Due to its polymorphic manifestation and subtle endoscopic presentation, the diagnosis is difficult and thus the disorder is underreported. We aimed to use deep learning to identify celiac disease on endoscopic images of the small bowel. Methods Patients with small intestinal histology compatible with CD (MARSH classification I-III) were extracted retrospectively from the database of Augsburg University hospital. They were compared to patients with no clinical signs of CD and histologically normal small intestinal mucosa. In a first step MARSH III and normal small intestinal mucosa were differentiated with the help of a deep learning algorithm. For this, the endoscopic white light images were divided into five equal-sized subsets. We avoided splitting the images of one patient into several subsets. A ResNet-50 model was trained with the images from four subsets and then validated with the remaining subset. This process was repeated for each subset, such that each subset was validated once. Sensitivity, specificity, and harmonic mean (F1) of the algorithm were determined. Results The algorithm showed values of 0.83, 0.88, and 0.84 for sensitivity, specificity, and F1, respectively. Further data showing a comparison between the detection rate of the AI model and that of experienced endoscopists will be available at the time of the upcoming conference. Conclusions We present the first clinical report on the use of a deep learning algorithm for the detection of celiac disease using endoscopic images. Further evaluation on an external data set, as well as in the detection of CD in real-time, will follow. However, this work at least suggests that AI can assist endoscopists in the endoscopic diagnosis of CD, and ultimately may be able to do a true optical biopsy in live-time. KW - Celiac Disease KW - Deep Learning Y1 - 2021 U6 - https://doi.org/10.1055/s-0041-1724970 N1 - Digital poster exhibition VL - 53 IS - S 01 PB - Georg Thieme Verlag CY - Stuttgart ER - TY - JOUR A1 - Ruewe, Marc A1 - Eigenberger, Andreas A1 - Klein, Silvan A1 - von Riedheim, Antonia A1 - Gugg, Christine A1 - Prantl, Lukas A1 - Palm, Christoph A1 - Weiherer, Maximilian A1 - Zeman, Florian A1 - Anker, Alexandra T1 - Precise Monitoring of Returning Sensation in Digital Nerve Lesions by 3-D Imaging: A Proof-of-Concept Study JF - Plastic and Reconstructive Surgery N2 - Digital nerve lesions result in a loss of tactile sensation reflected by an anesthetic area (AA) at the radial or ulnar aspect of the respective digit. Yet, available tools to monitor the recovery of tactile sense have been criticized for their lack of validity. However, the precise quantification of AA dynamics by three-dimensional (3-D) imaging could serve as an accurate surrogate to monitor recovery following digital nerve repair. For validation, AAs were marked on digits of healthy volunteers to simulate the AA of an impaired cutaneous innervation. Three dimensional models were composed from raw images that had been acquired with a 3-D camera (Vectra H2) to precisely quantify relative AA for each digit (3-D models, n= 80). Operator properties varied regarding individual experience in 3-D imaging and image processing. Additionally, the concept was applied in a clinical case study. Images taken by experienced photographers were rated better quality (p< 0.001) and needed less processing time (p= 0.020). Quantification of the relative AA was neither altered significantly by experience levels of the photographer (p= 0.425) nor the image assembler (p= 0.749). The proposed concept allows precise and reliable surface quantification of digits and can be performed consistently without relevant distortion by lack of examiner experience. Routine 3-D imaging of the AA has the great potential to provide visual evidence of various returning states of sensation and to convert sensory nerve recovery into a metric variable with high responsiveness to temporal progress. KW - 3D imaging Y1 - 2023 U6 - https://doi.org/10.1097/PRS.0000000000010456 SN - 1529-4242 VL - 152 IS - 4 SP - 670e EP - 674e PB - Lippincott Williams & Wilkins CY - Philadelphia, Pa. ER - TY - INPR A1 - Weiherer, Maximilian A1 - von Riedheim, Antonia A1 - Brébant, Vanessa A1 - Egger, Bernhard A1 - Palm, Christoph T1 - iRBSM: A Deep Implicit 3D Breast Shape Model N2 - We present the first deep implicit 3D shape model of the female breast, building upon and improving the recently proposed Regensburg Breast Shape Model (RBSM). Compared to its PCA-based predecessor, our model employs implicit neural representations; hence, it can be trained on raw 3D breast scans and eliminates the need for computationally demanding non-rigid registration -- a task that is particularly difficult for feature-less breast shapes. The resulting model, dubbed iRBSM, captures detailed surface geometry including fine structures such as nipples and belly buttons, is highly expressive, and outperforms the RBSM on different surface reconstruction tasks. Finally, leveraging the iRBSM, we present a prototype application to 3D reconstruct breast shapes from just a single image. Model and code publicly available at this https URL. KW - Shape Model KW - Female Breast KW - Regensburg Breast Shape Model Y1 - 2024 U6 - https://doi.org/10.48550/arXiv.2412.13244 ER - TY - GEN A1 - Ebigbo, Alanna A1 - Mendel, Robert A1 - Tziatzios, Georgios A1 - Probst, Andreas A1 - Palm, Christoph A1 - Messmann, Helmut T1 - Real-Time Diagnosis of an Early Barrett's Carcinoma using Artificial Intelligence (AI) - Video Case Demonstration T2 - Endoscopy N2 - Introduction We present a clinical case showing the real-time detection, characterization and delineation of an early Barrett’s cancer using AI. Patients and methods A 70-year old patient with a long-segment Barrett’s esophagus (C5M7) was assessed with an AI algorithm. Results The AI system detected a 10 mm focal lesion and AI characterization predicted cancer with a probability of >90%. After ESD resection, histopathology showed mucosal adenocarcinoma (T1a (m), R0) confirming AI diagnosis. Conclusion We demonstrate the real-time AI detection, characterization and delineation of a small and early mucosal Barrett’s cancer. KW - Artificial Intelligence KW - Barrett's Carcinoma KW - Speiseröhrenkrebs KW - Künstliche Intelligenz KW - Diagnose Y1 - 2020 U6 - https://doi.org/10.1055/s-0040-1704075 VL - 52 IS - S 01 PB - Thieme ER - TY - CHAP A1 - Nunes, Danilo Weber A1 - Hammer, Michael A1 - Hammer, Simone A1 - Uller, Wibke A1 - Palm, Christoph T1 - Classification of Vascular Malformations Based on T2 STIR Magnetic Resonance Imaging T2 - Bildverarbeitung für die Medizin 2022: Proceedings, German Workshop on Medical Image Computing, Heidelberg, June 26-28, 2022 N2 - Vascular malformations (VMs) are a rare condition. They can be categorized into high-flow and low-flow VMs, which is a challenging task for radiologists. In this work, a very heterogeneous set of MRI images with only rough annotations are used for classification with a convolutional neural network. The main focus is to describe the challenging data set and strategies to deal with such data in terms of preprocessing, annotation usage and choice of the network architecture. We achieved a classification result of 89.47 % F1-score with a 3D ResNet 18. KW - Deep Learning KW - Magnetic Resonance Imaging KW - Vascular Malformations Y1 - 2022 U6 - https://doi.org/10.1007/978-3-658-36932-3_57 SP - 267 EP - 272 PB - Springer Vieweg CY - Wiesbaden ER - TY - CHAP A1 - Rauber, David A1 - Mendel, Robert A1 - Scheppach, Markus W. A1 - Ebigbo, Alanna A1 - Messmann, Helmut A1 - Palm, Christoph T1 - Analysis of Celiac Disease with Multimodal Deep Learning T2 - Bildverarbeitung für die Medizin 2022: Proceedings, German Workshop on Medical Image Computing, Heidelberg, June 26-28, 2022 N2 - Celiac disease is an autoimmune disorder caused by gluten that results in an inflammatory response of the small intestine.We investigated whether celiac disease can be detected using endoscopic images through a deep learning approach. The results show that additional clinical parameters can improve the classification accuracy. In this work, we distinguished between healthy tissue and Marsh III, according to the Marsh score system. We first trained a baseline network to classify endoscopic images of the small bowel into these two classes and then augmented the approach with a multimodality component that took the antibody status into account. KW - Deep Learning KW - Endoscopy Y1 - 2022 U6 - https://doi.org/10.1007/978-3-658-36932-3_25 SP - 115 EP - 120 PB - Springer Vieweg CY - Wiesbaden ER - TY - CHAP A1 - Weber, Joachim A1 - Brawanski, Alexander A1 - Palm, Christoph T1 - Parallelization of FSL-Fast segmentation of MRI brain data T2 - 58. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie e.V. (GMDS 2013), Lübeck, 01.-05.09.2013 Y1 - 2013 U6 - https://doi.org/10.3205/13gmds261 N1 - Meeting Abstract IS - DocAbstr. 329 PB - German Medical Science GMS Publishing House CY - Düsseldorf ER - TY - GEN ED - Lehmann, Thomas M. ED - Palm, Christoph ED - Spitzer, Klaus ED - Tolxdorff, Thomas T1 - Advances in Quantitative Laryngoscopy, Voice and Speech Research, Procs. 3rd International Workshop, RWTH Aachen Y1 - 1998 CY - Aachen ER - TY - JOUR A1 - Huber, Michaela A1 - Schlosser, Daniela A1 - Stenzel, Susanne A1 - Maier, Johannes A1 - Pattappa, Girish A1 - Kujat, Richard A1 - Striegl, Birgit A1 - Docheva, Denitsa T1 - Quantitative Analysis of Surface Contouring with Pulsed Bipolar Radiofrequency on Thin Chondromalacic Cartilage JF - BioMed Research International N2 - The purpose of this study was to evaluate the quality of surface contouring of chondromalacic cartilage by bipolar radio frequency energy using different treatment patterns in an animal model, as well as examining the impact of the treatment onto chondrocyte viability by two different methods. Our experiments were conducted on 36 fresh osteochondral sections from the tibia plateau of slaughtered 6-month-old pigs, where the thickness of the cartilage is similar to that of human wrist cartilage. An area of 1 cm(2) was first treated with emery paper to simulate the chondromalacic cartilage. Then, the treatment with RFE followed in 6 different patterns. The osteochondral sections were assessed for cellular viability (live/dead assay, caspase (cell apoptosis marker) staining, and quantitative analysed images obtained by fluorescent microscopy). For a quantitative characterization of none or treated cartilage surfaces, various roughness parameters were measured using confocal laser scanning microscopy (Olympus LEXT OLS 4000 3D). To describe the roughness, the Root-Mean-Square parameter (Sq) was calculated. A smoothing effect of the cartilage surface was detectable upon each pattern of RFE treatment. The Sq for native cartilage was Sq=3.8 +/- 1.1 mu m. The best smoothing pattern was seen for two RFE passes and a 2-second pulsed mode (B2p2) with an Sq=27.3 +/- 4.9 mu m. However, with increased smoothing, an augmentation in chondrocyte death up to 95% was detected. Using bipolar RFE treatment in arthroscopy for small joints like the wrist or MCP joints should be used with caution. In the case of chondroplasty, there is a high chance to destroy the joint cartilage. KW - CHONDROCYTE DEATH KW - energy KW - HUMAN ARTICULAR-CARTILAGE KW - MONOPOLAR KW - THERMAL CHONDROPLASTY Y1 - 2020 U6 - https://doi.org/10.1155/2020/1242086 SP - 1 EP - 8 PB - HINDAWI ER - TY - CHAP A1 - Palm, Christoph A1 - Siegmund, Heiko A1 - Semmelmann, Matthias A1 - Grafe, Claudia A1 - Evert, Matthias A1 - Schroeder, Josef A. T1 - Interactive Computer-assisted Approach for Evaluation of Ultrastructural Cilia Abnormalities T2 - Medical Imaging 2016: Computer-Aided Diagnosis, San Diego, California, United States, 27 February - 3 March, SPIE Proceedings 97853N, 2016, ISBN 9781510600201 N2 - Introduction – Diagnosis of abnormal cilia function is based on ultrastructural analysis of axoneme defects, especialy the features of inner and outer dynein arms which are the motors of ciliar motility. Sub-optimal biopsy material, methodical, and intrinsic electron microscopy factors pose difficulty in ciliary defects evaluation. We present a computer-assisted approach based on state-of-the-art image analysis and object recognition methods yielding a time-saving and efficient diagnosis of cilia dysfunction. Method – The presented approach is based on a pipeline of basal image processing methods like smoothing, thresholding and ellipse fitting. However, integration of application specific knowledge results in robust segmentations even in cases of image artifacts. The method is build hierarchically starting with the detection of cilia within the image, followed by the detection of nine doublets within each analyzable cilium, and ending with the detection of dynein arms of each doublet. The process is concluded by a rough classification of the dynein arms as basis for a computer-assisted diagnosis. Additionally, the interaction possibilities are designed in a way, that the results are still reproducible given the completion report. Results – A qualitative evaluation showed reasonable detection results for cilia, doublets and dynein arms. However, since a ground truth is missing, the variation of the computer-assisted diagnosis should be within the subjective bias of human diagnosticians. The results of a first quantitative evaluation with five human experts and six images with 12 analyzable cilia showed, that with default parameterization 91.6% of the cilia and 98% of the doublets were found. The computer-assisted approach rated 66% of those inner and outer dynein arms correct, where all human experts agree. However, especially the quality of the dynein arm classification may be improved in future work. KW - Image analysis KW - Image processing KW - Computer aided diagnosis and therapy KW - Image classification KW - Image segmentation KW - Biopsy KW - Electron microscopy KW - Zilie KW - Ultrastruktur KW - Anomalie KW - Bildverarbeitung KW - Objekterkennung KW - Computerunterstütztes Verfahren Y1 - 2016 U6 - https://doi.org/10.1117/12.2214976 ER - TY - JOUR A1 - Hutterer, Markus A1 - Hattingen, Elke A1 - Palm, Christoph A1 - Proescholdt, Martin Andreas A1 - Hau, Peter T1 - Current standards and new concepts in MRI and PET response assessment of antiangiogenic therapies in high-grade glioma patients JF - Neuro-Oncology N2 - Despite multimodal treatment, the prognosis of high-grade gliomas is grim. As tumor growth is critically dependent on new blood vessel formation, antiangiogenic treatment approaches offer an innovative treatment strategy. Bevacizumab, a humanized monoclonal antibody, has been in the spotlight of antiangiogenic approaches for several years. Currently, MRI including contrast-enhanced T1-weighted and T2/fluid-attenuated inversion recovery (FLAIR) images is routinely used to evaluate antiangiogenic treatment response (Response Assessment in Neuro-Oncology criteria). However, by restoring the blood–brain barrier, bevacizumab may reduce T1 contrast enhancement and T2/FLAIR hyperintensity, thereby obscuring the imaging-based detection of progression. The aim of this review is to highlight the recent role of imaging biomarkers from MR and PET imaging on measurement of disease progression and treatment effectiveness in antiangiogenic therapies. Based on the reviewed studies, multimodal imaging combining standard MRI with new physiological MRI techniques and metabolic PET imaging, in particular amino acid tracers, may have the ability to detect antiangiogenic drug susceptibility or resistance prior to morphological changes. As advances occur in the development of therapies that target specific biochemical or molecular pathways and alter tumor physiology in potentially predictable ways, the validation of physiological and metabolic imaging biomarkers will become increasingly important in the near future. KW - High-grade glioma KW - Antiangiogenic treatment KW - MRI KW - PET KW - Multimodal response assessment KW - Gliom KW - Antiangiogenese KW - Bildgebendes Verfahren KW - Biomarker Y1 - 2015 U6 - https://doi.org/10.1093/neuonc/nou322 VL - 17 IS - 6 SP - 784 EP - 800 ER - TY - CHAP A1 - Mendel, Robert A1 - Rauber, David A1 - Palm, Christoph T1 - Exploring the Effects of Contrastive Learning on Homogeneous Medical Image Data T2 - Bildverarbeitung für die Medizin 2023: Proceedings, German Workshop on Medical Image Computing, July 2– 4, 2023, Braunschweig N2 - We investigate contrastive learning in a multi-task learning setting classifying and segmenting early Barrett’s cancer. How can contrastive learning be applied in a domain with few classes and low inter-class and inter-sample variance, potentially enabling image retrieval or image attribution? We introduce a data sampling strategy that mines per-lesion data for positive samples and keeps a queue of the recent projections as negative samples. We propose a masking strategy for the NT-Xent loss that keeps the negative set pure and removes samples from the same lesion. We show cohesion and uniqueness improvements of the proposed method in feature space. The introduction of the auxiliary objective does not affect the performance but adds the ability to indicate similarity between lesions. Therefore, the approach could enable downstream auto-documentation tasks on homogeneous medical image data. Y1 - 2023 U6 - https://doi.org/10.1007/978-3-658-41657-7 SP - 128 EP - 13 PB - Springer Vieweg CY - Wiesbaden ER - TY - GEN ED - Palm, Christoph ED - Deserno, Thomas M. ED - Handels, Heinz ED - Maier, Andreas ED - Maier-Hein, Klaus H. ED - Tolxdorff, Thomas T1 - Bildverarbeitung für die Medizin 2021 BT - Proceedings, German Workshop on Medical Image Computing, Regensburg, March 7–9, 2021 N2 - In den letzten Jahren hat sich der Workshop "Bildverarbeitung für die Medizin" durch erfolgreiche Veranstaltungen etabliert. Ziel ist auch 2021 wieder die Darstellung aktueller Forschungsergebnisse und die Vertiefung der Gespräche zwischen Wissenschaftlern, Industrie und Anwendern. Die Beiträge dieses Bandes - einige davon in englischer Sprache - umfassen alle Bereiche der medizinischen Bildverarbeitung, insbesondere Bildgebung und -akquisition, Maschinelles Lernen, Bildsegmentierung und Bildanalyse, Visualisierung und Animation, Zeitreihenanalyse, Computerunterstützte Diagnose, Biomechanische Modellierung, Validierung und Qualitätssicherung, Bildverarbeitung in der Telemedizin u.v.m. KW - Bildanalyse KW - Bildverarbeitung KW - Computerunterstützte Medizin KW - Deep Learning KW - Visualisierung Y1 - 2021 SN - 978-3-658-33197-9 U6 - https://doi.org/10.1007/978-3-658-33198-6 SN - 1431-472X PB - Springer Vieweg CY - Wiesbdaden ER - TY - GEN A1 - Mendel, Robert A1 - Souza Jr., Luis Antonio de A1 - Rauber, David A1 - Papa, João Paulo A1 - Palm, Christoph T1 - Abstract: Semi-supervised Segmentation Based on Error-correcting Supervision T2 - Bildverarbeitung für die Medizin 2021. Proceedings, German Workshop on Medical Image Computing, Regensburg, March 7-9, 2021 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. KW - Deep Learning Y1 - 2021 SN - 978-3-658-33197-9 U6 - https://doi.org/10.1007/978-3-658-33198-6_43 SP - 178 PB - Springer Vieweg CY - Wiesbaden ER - TY - JOUR A1 - Ebigbo, Alanna A1 - Palm, Christoph A1 - Messmann, Helmut T1 - Barrett esophagus: What to expect from Artificial Intelligence? JF - Best Practice & Research Clinical Gastroenterology N2 - The evaluation and assessment of Barrett’s esophagus is challenging for both expert and nonexpert endoscopists. However, the early diagnosis of cancer in Barrett’s esophagus is crucial for its prognosis, and could save costs. Pre-clinical and clinical studies on the application of Artificial Intelligence (AI) in Barrett’s esophagus have shown promising results. In this review, we focus on the current challenges and future perspectives of implementing AI systems in the management of patients with Barrett’s esophagus. KW - Deep Learning KW - Künstliche Intelligenz KW - Computerunterstützte Medizin KW - Barrett KW - Adenocarcinoma KW - Artificial intelligence KW - Deep learning KW - Convolutional neural networks Y1 - 2021 U6 - https://doi.org/10.1016/j.bpg.2021.101726 SN - 1521-6918 VL - 52-53 IS - June-August 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 - Guest editorial of the IJCARS - BVM 2018 special issue JF - International Journal of Computer Assisted Radiology and Surgery KW - Medical Image Computing Y1 - 2019 U6 - https://doi.org/10.1007/s11548-018-01902-0 VL - 14 SP - 1 EP - 2 PB - Springer ER - TY - JOUR A1 - Passos, Leandro A. A1 - Souza Jr., Luis Antonio de A1 - Mendel, Robert A1 - Ebigbo, Alanna A1 - Probst, Andreas A1 - Messmann, Helmut A1 - Palm, Christoph A1 - Papa, João Paulo T1 - Barrett's esophagus analysis using infinity Restricted Boltzmann Machines JF - Journal of Visual Communication and Image Representation N2 - The number of patients with Barret’s esophagus (BE) has increased in the last decades. Considering the dangerousness of the disease and its evolution to adenocarcinoma, an early diagnosis of BE may provide a high probability of cancer remission. However, limitations regarding traditional methods of detection and management of BE demand alternative solutions. As such, computer-aided tools have been recently used to assist in this problem, but the challenge still persists. To manage the problem, we introduce the infinity Restricted Boltzmann Machines (iRBMs) to the task of automatic identification of Barrett’s esophagus from endoscopic images of the lower esophagus. Moreover, since iRBM requires a proper selection of its meta-parameters, we also present a discriminative iRBM fine-tuning using six meta-heuristic optimization techniques. We showed that iRBMs are suitable for the context since it provides competitive results, as well as the meta-heuristic techniques showed to be appropriate for such task. KW - Speiseröhrenkrankheit KW - Diagnose KW - Boltzmann-Maschine KW - Barrett’s esophagus KW - Infinity Restricted Boltzmann Machines KW - Meta-heuristics KW - Deep learning KW - Metaheuristik KW - Maschinelles Lernen Y1 - 2019 U6 - https://doi.org/10.1016/j.jvcir.2019.01.043 VL - 59 SP - 475 EP - 485 PB - Elsevier ER - TY - CHAP A1 - Souza Jr., Luis Antonio de A1 - Afonso, Luis Claudio Sugi A1 - Palm, Christoph A1 - Papa, João Paulo T1 - Barrett's Esophagus Identification Using Optimum-Path Forest T2 - Proceedings of the 30th Conference on Graphics, Patterns and Images Tutorials (SIBGRAPI-T 2017), Niterói, Rio de Janeiro, Brazil, 2017, 17-20 October N2 - Computer-assisted analysis of endoscopic images can be helpful to the automatic diagnosis and classification of neoplastic lesions. Barrett's esophagus (BE) is a common type of reflux that is not straight forward to be detected by endoscopic surveillance, thus being way susceptible to erroneous diagnosis, which can cause cancer when not treated properly. In this work, we introduce the Optimum-Path Forest (OPF) classifier to the task of automatic identification of Barrett'sesophagus, with promising results and outperforming the well known Support Vector Machines (SVM) in the aforementioned context. We consider describing endoscopic images by means of feature extractors based on key point information, such as the Speeded up Robust Features (SURF) and Scale-Invariant Feature Transform (SIFT), for further designing a bag-of-visual-wordsthat is used to feed both OPF and SVM classifiers. The best results were obtained by means of the OPF classifier for both feature extractors, with values lying on 0.732 (SURF) - 0.735(SIFT) for sensitivity, 0.782 (SURF) - 0.806 (SIFT) for specificity, and 0.738 (SURF) - 0.732 (SIFT) for the accuracy. KW - Speiseröhrenkrankheit KW - Diagnose KW - Maschinelles Lernen KW - Bilderkennung KW - Automatische Klassifikation Y1 - 2017 U6 - https://doi.org/10.1109/SIBGRAPI.2017.47 SP - 308 EP - 314 ER - TY - CHAP A1 - Zehner, Alexander A1 - Szalo, Alexander Eduard A1 - Palm, Christoph T1 - GraphMIC: Easy Prototyping of Medical Image Computing Applications T2 - Interactive Medical Image Computing (IMIC), Workshop at the Medical Image Computing and Computer Assisted Interventions (MICCAI 2015), 2015, Munich N2 - GraphMIC is a cross-platform image processing application utilizing the libraries ITK and OpenCV. The abstract structure of image processing pipelines is visually represented by user interface components based on modern QtQuick technology and allows users to focus on arrangement and parameterization of operations rather than implementing the equivalent functionality natively in C++. The application's central goal is to improve and simplify the typical workflow by providing various high level features and functions like multi threading, image sequence processing and advanced error handling. A built-in python interpreter allows the creation of custom nodes, where user defined algorithms can be integrated to extend basic functionality. An embedded 2d/3d visual-izer gives feedback of the resulting image of an operation or the whole pipeline. User inputs like seed points, contours or regions are forwarded to the processing pipeline as parameters to offer semi-automatic image computing. We report the main concept of the application and introduce several features and their implementation. Finally, the current state of development as well as future perspectives of GraphMIC are discussed KW - Bildverarbeitung KW - Medizin Y1 - 2015 U6 - https://doi.org/10.13140/RG.2.1.3718.4725 N1 - Open-Access-Publikation SP - 395 EP - 400 ER -