The Liver Tumor Segmentation Benchmark (LiTS)

In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LiTS), which was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2017 and the International Conferences on Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2017 and 2018. The image dataset is diverse and contains primary and secondary tumors with varied sizes and appearances with various lesion-to-background levels (hyper-/hypo-dense), created in collaboration with seven hospitals and research institutions. Seventy-five submitted liver and liver tumor segmentation algorithms were trained on a set of 131 computed tomography (CT) volumes and were tested on 70 unseen test images acquired from different patients. We found that not a single algorithm performed best for both liver and liver tumors in the three events. The best liver segmentation algorithm achieved a Dice score of 0.963, whereas, for tumor segmentation, the best algorithms achieved Dices scores of 0.674 (ISBI 2017), 0.702 (MICCAI 2017), and 0.739 (MICCAI 2018). Retrospectively, we performed additional analysis on liver tumor detection and revealed that not all top-performing segmentation algorithms worked well for tumor detection. The best liver tumor detection method achieved a lesion-wise recall of 0.458 (ISBI 2017), 0.515 (MICCAI 2017), and 0.554 (MICCAI 2018), indicating the need for further research. LiTS remains an active benchmark and resource for research, e.g., contributing the liver-related segmentation tasks in http://medicaldecathlon.com/. In addition, both data and online evaluation are accessible via https://competitions.codalab.org/competitions/17094.Bjoern Menze is supported through the DFG funding (SFB 824, subproject B12) and a Helmut-Horten-Professorship for Biomedical Informatics by the Helmut-Horten-Foundation. Florian Kofler is Supported by Deutsche Forschungsgemeinschaft (DFG) through TUM International Graduate School of Science and Engineering (IGSSE), GSC 81. An Tang was supported by the Fonds de recherche du Québec en Santé and Fondation de l’association des radiologistes du Québec (FRQS- ARQ 34939 Clinical Research Scholarship – Junior 2 Salary Award). Hongwei Bran Li is supported by Forschungskredit (Grant NO. FK-21- 125) from University of Zurich.Peer ReviewedArticle signat per 109 autors/es: Patrick Bilic 1,a,b, Patrick Christ 1,a,b, Hongwei Bran Li 1,2,∗,b, Eugene Vorontsov 3,a,b, Avi Ben-Cohen 5,a, Georgios Kaissis 10,12,15,a, Adi Szeskin 18,a, Colin Jacobs 4,a, Gabriel Efrain Humpire Mamani 4,a, Gabriel Chartrand 26,a, Fabian Lohöfer 12,a, Julian Walter Holch 29,30,69,a, Wieland Sommer 32,a, Felix Hofmann 31,32,a, Alexandre Hostettler 36,a, Naama Lev-Cohain 38,a, Michal Drozdzal 34,a, Michal Marianne Amitai 35,a, Refael Vivanti 37,a, Jacob Sosna 38,a, Ivan Ezhov 1, Anjany Sekuboyina 1,2, Fernando Navarro 1,76,78, Florian Kofler 1,13,57,78, Johannes C. Paetzold 15,16, Suprosanna Shit 1, Xiaobin Hu 1, Jana Lipková 17, Markus Rempfler 1, Marie Piraud 57,1, Jan Kirschke 13, Benedikt Wiestler 13, Zhiheng Zhang 14, Christian Hülsemeyer 1, Marcel Beetz 1, Florian Ettlinger 1, Michela Antonelli 9, Woong Bae 73, Míriam Bellver 43, Lei Bi 61, Hao Chen 39, Grzegorz Chlebus 62,64, Erik B. Dam 72, Qi Dou 41, Chi-Wing Fu 41, Bogdan Georgescu 60, Xavier Giró-i-Nieto 45, Felix Gruen 28, Xu Han 77, Pheng-Ann Heng 41, Jürgen Hesser 48,49,50, Jan Hendrik Moltz 62, Christian Igel 72, Fabian Isensee 69,70, Paul Jäger 69,70, Fucang Jia 75, Krishna Chaitanya Kaluva 21, Mahendra Khened 21, Ildoo Kim 73, Jae-Hun Kim 53, Sungwoong Kim 73, Simon Kohl 69, Tomasz Konopczynski 49, Avinash Kori 21, Ganapathy Krishnamurthi 21, Fan Li 22, Hongchao Li 11, Junbo Li 8, Xiaomeng Li 40, John Lowengrub 66,67,68, Jun Ma 54, Klaus Maier-Hein 69,70,7, Kevis-Kokitsi Maninis 44, Hans Meine 62,65, Dorit Merhof 74, Akshay Pai 72, Mathias Perslev 72, Jens Petersen 69, Jordi Pont-Tuset 44, Jin Qi 56, Xiaojuan Qi 40, Oliver Rippel 74, Karsten Roth 47, Ignacio Sarasua 51,12, Andrea Schenk 62,63, Zengming Shen 59,60, Jordi Torres 46,43, Christian Wachinger 51,12,1, Chunliang Wang 42, Leon Weninger 74, Jianrong Wu 25, Daguang Xu 71, Xiaoping Yang 55, Simon Chun-Ho Yu 58, Yading Yuan 52, Miao Yue 20, Liping Zhang 58, Jorge Cardoso 9, Spyridon Bakas 19,23,24, Rickmer Braren 6,12,30,a, Volker Heinemann 33,a, Christopher Pal 3,a, An Tang 27,a, Samuel Kadoury 3,a, Luc Soler 36,a, Bram van Ginneken 4,a, Hayit Greenspan 5,a, Leo Joskowicz 18,a, Bjoern Menze 1,2,a // 1 Department of Informatics, Technical University of Munich, Germany; 2 Department of Quantitative Biomedicine, University of Zurich, Switzerland; 3 Ecole Polytechnique de Montréal, Canada; 4 Department of Medical Imaging, Radboud University Medical Center, Nijmegen, The Netherlands; 5 Department of Biomedical Engineering, Tel-Aviv University, Israel; 6 German Cancer Consortium (DKTK), Germany; 7 Pattern Analysis and Learning Group, Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany; 8 Philips Research China, Philips China Innovation Campus, Shanghai, China; 9 School of Biomedical Engineering & Imaging Sciences, King’s College London, London, UK; 10 Institute for AI in Medicine, Technical University of Munich, Germany; 11 Department of Computer Science, Guangdong University of Foreign Studies, China; 12 Institute for diagnostic and interventional radiology, Klinikum rechts der Isar, Technical University of Munich, Germany; 13 Institute for diagnostic and interventional neuroradiology, Klinikum rechts der Isar,Technical University of Munich, Germany; 14 Department of Hepatobiliary Surgery, the Affiliated Drum Tower Hospital of Nanjing University Medical School, China; 15 Department of Computing, Imperial College London, London, United Kingdom; 16 Institute for Tissue Engineering and Regenerative Medicine, Helmholtz Zentrum München, Neuherberg, Germany; 17 Brigham and Women’s Hospital, Harvard Medical School, USA; 18 School of Computer Science and Engineering, the Hebrew University of Jerusalem, Israel; 19 Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, PA, USA; 20 CGG Services (Singapore) Pte. Ltd., Singapore; 21 Medical Imaging and Reconstruction Lab, Department of Engineering Design, Indian Institute of Technology Madras, India; 22 Sensetime, Shanghai, China; 23 Department of Radiology, Perelman School of Medicine, University of Pennsylvania, USA; 24 Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, PA, USA; 25 Tencent Healthcare (Shenzhen) Co., Ltd, China; 26 The University of Montréal Hospital Research Centre (CRCHUM) Montréal, Québec, Canada; 27 Department of Radiology, Radiation Oncology and Nuclear Medicine, University of Montréal, Canada; 28 Institute of Control Engineering, Technische Universität Braunschweig, Germany; 29 Department of Medicine III, University Hospital, LMU Munich, Munich, Germany; 30 Comprehensive Cancer Center Munich, Munich, Germany; 31 Department of General, Visceral and Transplantation Surgery, University Hospital, LMU Munich, Germany; 32 Department of Radiology, University Hospital, LMU Munich, Germany; 33 Department of Hematology/Oncology & Comprehensive Cancer Center Munich, LMU Klinikum Munich, Germany; 34 Polytechnique Montréal, Mila, QC, Canada; 35 Department of Diagnostic Radiology, Sheba Medical Center, Tel Aviv university, Israel; 36 Department of Surgical Data Science, Institut de Recherche contre les Cancers de l’Appareil Digestif (IRCAD), France; 37 Rafael Advanced Defense System, Israel; 38 Department of Radiology, Hadassah University Medical Center, Jerusalem, Israel; 39 Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, China; 40 Department of Electrical and Electronic Engineering, The University of Hong Kong, China; 41 Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong, China; 42 Department of Biomedical Engineering and Health Systems, KTH Royal Institute of Technology, Sweden; 43 Barcelona Supercomputing Center, Barcelona, Spain; 44 Eidgenössische Technische Hochschule Zurich (ETHZ), Zurich, Switzerland; 45 Signal Theory and Communications Department, Universitat Politecnica de Catalunya, Catalonia, Spain; 46 Universitat Politecnica de Catalunya, Catalonia, Spain; 47 University of Tuebingen, Germany; 48 Mannheim Institute for Intelligent Systems in Medicine, department of Medicine Mannheim, Heidelberg University, Germany; 49 Interdisciplinary Center for Scientific Computing (IWR), Heidelberg University, Germany; 50 Central Institute for Computer Engineering (ZITI), Heidelberg University, Germany; 51 Department of Child and Adolescent Psychiatry, Ludwig-Maximilians-Universität, Munich, Germany; 52 Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, NY, USA; 53 Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, South Korea; 54 Department of Mathematics, Nanjing University of Science and Technology, China; 55 Department of Mathematics, Nanjing University, China; 56 School of Information and Communication Engineering, University of Electronic Science and Technology of China, China; 57 Helmholtz AI, Helmholtz Zentrum München, Neuherberg, Germany; 58 Department of Imaging and Interventional Radiology, Chinese University of Hong Kong, Hong Kong, China; 59 Beckman Institute, University of Illinois at Urbana-Champaign, USA; 60 Siemens Healthineers, USA; 61 School of Computer Science, the University of Sydney, Australia; 62 Fraunhofer MEVIS, Bremen, Germany; 63 Institute for Diagnostic and Interventional Radiology, Hannover Medical School, Hannover, Germany; 64 Diagnostic Image Analysis Group, Radboud University Medical Center, Nijmegen, The Netherlands; 65 Medical Image Computing Group, FB3, University of Bremen, Germany; 66 Departments of Mathematics, Biomedical Engineering, University of California, Irvine, USA; 67 Center for Complex Biological Systems, University of California, Irvine, USA; 68 Chao Family Comprehensive Cancer Center, University of California, Irvine, USA; 69 Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany; 70 Helmholtz Imaging, Germany; 71 NVIDIA, Santa Clara, CA, USA; 72 Department of Computer Science, University of Copenhagen, Denmark; 73 Kakao Brain, Republic of Korea; 74 Institute of Imaging & Computer Vision, RWTH Aachen University, Germany; 75 Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, China; 76 Department of Radiation Oncology and Radiotherapy, Klinikum rechts der Isar, Technical University of Munich, Germany; 77 Department of computer science, UNC Chapel Hill, USA; 78 TranslaTUM - Central Institute for Translational Cancer Research, Technical University of Munich, GermanyPostprint (published version

The Liver Tumor Segmentation Benchmark (LiTS)

In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LiTS), which was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2017 and the International Conferences on Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2017 and 2018. The image dataset is diverse and contains primary and secondary tumors with varied sizes and appearances with various lesion-to-background levels (hyper-/hypo-dense), created in collaboration with seven hospitals and research institutions. Seventy-five submitted liver and liver tumor segmentation algorithms were trained on a set of 131 computed tomography (CT) volumes and were tested on 70 unseen test images acquired from different patients. We found that not a single algorithm performed best for both liver and liver tumors in the three events. The best liver segmentation algorithm achieved a Dice score of 0.963, whereas, for tumor segmentation, the best algorithms achieved Dices scores of 0.674 (ISBI 2017), 0.702 (MICCAI 2017), and 0.739 (MICCAI 2018). Retrospectively, we performed additional analysis on liver tumor detection and revealed that not all top-performing segmentation algorithms worked well for tumor detection. The best liver tumor detection method achieved a lesion-wise recall of 0.458 (ISBI 2017), 0.515 (MICCAI 2017), and 0.554 (MICCAI 2018), indicating the need for further research. LiTS remains an active benchmark and resource for research, e.g., contributing the liver-related segmentation tasks in http://medicaldecathlon.com/. In addition, both data and online evaluation are accessible via https://competitions.codalab.org/competitions/17094

The Liver Tumor Segmentation Benchmark (LiTS)

In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LITS) organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2016 and International Conference On Medical Image Computing Computer Assisted Intervention (MICCAI) 2017. Twenty four valid state-of-the-art liver and liver tumor segmentation algorithms were applied to a set of 131 computed tomography (CT) volumes with different types of tumor contrast levels (hyper-/hypo-intense), abnormalities in tissues (metastasectomie) size and varying amount of lesions. The submitted algorithms have been tested on 70 undisclosed volumes. The dataset is created in collaboration with seven hospitals and research institutions and manually reviewed by independent three radiologists. We found that not a single algorithm performed best for liver and tumors. The best liver segmentation algorithm achieved a Dice score of 0.96(MICCAI) whereas for tumor segmentation the best algorithm evaluated at 0.67(ISBI) and 0.70(MICCAI). The LITS image data and manual annotations continue to be publicly available through an online evaluation system as an ongoing benchmarking resource.Comment: conferenc

DefED-Net: Deformable Encoder-Decoder Network for Liver and Liver Tumor Segmentation

Deep convolutional neural networks have been widely used for medical image segmentation due to their superiority in feature learning. Although these networks are successful for simple object segmentation tasks, they suffer from two problems for liver and liver tumor segmentation in CT images. One is that convolutional kernels of fixed geometrical structure are unmatched with livers and liver tumors of irregular shapes. The other is that pooling and strided convolutional operations easily lead to the loss of spatial contextual information of images. To address these issues, we propose a deformable encoder-decoder network (DefED-Net) for liver and liver tumor segmentation. The proposed network makes two contributions. The first is that the deformable convolution is used to enhance the feature representation capability of DefED-Net, which can help the network to learn convolution kernels with adaptive spatial structuring information. The second is that we design a Ladder-atrous-spatial-pyramid-pooling module using multi-scale dilation rate (Ladder-ASPP) and apply the module to learn better context information than the atrous spatial pyramid pooling (ASPP) for CT image segmentation. The proposed DefED-Net is evaluated on two public benchmark datasets, the LiTS and the 3DIRCADb. Experiments demonstrate that the DefED-Net has better capability of feature representation as well as provides higher accuracy on liver and liver tumor segmentation than stateof-the art networks. The available code of DefED-Net we propose can be found from https://github.com/SUST-reynole/DefED-Net.Science and Technology Program of Shaanxi Province of China; National Natural Science Foundation of Chin

On Medical Image Segmentation and on Modeling Long Term Dependencies

La délimitation (segmentation) des tumeurs malignes à partir d’images médicales est importante pour le diagnostic du cancer, la planification des traitements ciblés, ainsi que les suivis de la progression du cancer et de la réponse aux traitements. Cependant, bien que la segmentation manuelle des images médicales soit précise, elle prend du temps, nécessite des opérateurs experts et est souvent peu pratique lorsque de grands ensembles de données sont utilisés. Ceci démontre la nécessité d’une segmentation automatique. Cependant, la segmentation automatisée des tumeurs est particulièrement difficile en raison de la variabilité de l’apparence des tumeurs, de l’équipement d’acquisition d’image et des paramètres d’acquisition, et de la variabilité entre les patients. Les tumeurs varient en type, taille, emplacement et quantité; le reste de l’image varie en raison des différences anatomiques entre les patients, d’une chirurgie antérieure ou d’une thérapie ablative, de différences dans l’amélioration du contraste des tissus et des artefacts d’image. De plus, les protocoles d’acquisition du scanner varient considérablement entre les cliniques et les caractéristiques de l’image varient selon le modèle du scanner. En raison de toutes ces variabilités, un modèle de segmentation doit être suffisamment flexible pour apprendre les caractéristiques générales des données. L’avènement des réseaux profonds de neurones à convolution (convolutional neural networks, CNN) a permis une classification exacte et précise des images hautement variables et, par extension, une segmentation de haute qualité des images. Cependant, ces modèles doivent être formés sur d’énormes quantités de données étiquetées. Cette contrainte est particulièrement difficile dans le contexte de la segmentation des images médicales, car le nombre de segmentations pouvant être produites est limité dans la pratique par la nécessité d’employer des opérateurs experts pour réaliser un tel étiquetage. De plus, les variabilités d’intérêt dans les images médicales semblent suivre une distribution à longue traîne, ce qui signifie qu’un nombre particulièrement important de données utilisées pour l’entraînement peut être nécessaire pour fournir un échantillon suffisant de chaque type de variabilité à un CNN. Cela démontre la nécessité de développer des stratégies pour la formation de ces modèles avec des segmentations de vérité-terrain disponibles limitées.----------ABSTRACT: The delineation (segmentation) of malignant tumours in medical images is important for cancer diagnosis, the planning of targeted treatments, and the tracking of cancer progression and treatment response. However, although manual segmentation of medical images is accurate, it is time consuming, requires expert operators, and is often impractical with large datasets. This motivates the need for training automated segmentation. However, automated segmentation of tumours is particularly challenging due to variability in tumour appearance, image acquisition equipment and acquisition parameters, and variability across patients. Tumours vary in type, size, location, and quantity; the rest of the image varies due to anatomical differences between patients, prior surgery or ablative therapy, differences in contrast enhancement of tissues, and image artefacts. Furthermore, scanner acquisition protocols vary considerably between clinical sites and image characteristics vary according to the scanner model. Due to all of these variabilities, a segmentation model must be flexible enough to learn general features from the data. The advent of deep convolutional neural networks (CNN) allowed for accurate and precise classification of highly variable images and, by extension, of high quality segmentation images. However, these models must be trained on enormous quantities of labeled data. This constraint is particularly challenging in the context of medical image segmentation because the number of segmentations that can be produced is limited in practice by the need to employ expert operators to do such labeling. Furthermore, the variabilities of interest in medical images appear to follow a long tail distribution, meaning a particularly large amount of training data may be required to provide a sufficient sample of each type of variability to a CNN. This motivates the need to develop strategies for training these models with limited ground truth segmentations available

Actas de SABI2020

Los temas salientes incluyen un marcapasos pulmonar que promete complementar y eventualmente sustituir la conocida ventilación mecánica por presión positiva (intubación), el análisis de la marchaespontánea sin costosos equipamientos, las imágenes infrarrojas y la predicción de la salud cardiovascular en temprana edad por medio de la biomecánica arterial