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arXiv 2021年

作者： Han, Tianyu Kather, Jakob Nikolas Pedersoli, Federico Zimmermann, Markus Keil, Sebastian Schulze-Hagen, Maximilian Terwoelbeck, Marc Isfort, Peter Haarburger, Christoph Kiessling, Fabian Schulz, Volkmar Kuhl, Christiane Nebelung, Sven Truhn, Daniel Physics of Molecular Imaging Systems Experimental Molecular Imaging RWTH Aachen University Germany Department of Medicine III University Hospital RWTH Aachen Aachen Germany Pathology and Data Analytics Leeds Institute of Medical Research at St James’s University of Leeds Leeds United Kingdom Medical Oncology National Center for Tumor Diseases University Hospital Heidelberg Heidelberg Germany Department of Diagnostic and Interventional Radiology University Hospital Aachen Germany CheckupPoint GmbH Munich Germany University Hospital RWTH Aachen Aachen Germany Fraunhofer Institute for Digital Medicine MEVIS Bremen Germany The Institute for Experimental Molecular Imaging RWTH Aachen University Germany

Disease-modifying management aims to prevent deterioration and progression of the disease, not just relieve symptoms. Unfortunately, the development of necessary therapies is often hampered by the failure to recognize the presymptomatic disease and limited understanding of disease development. We present a generic solution for this problem by a methodology that allows the prediction of progression risk and morphology in individuals using a latent extrapolation optimization approach. To this end, we combined a regularized generative adversarial network (GAN) and a latent nearest neighbor algorithm for joint optimization to generate plausible images of future time points. We evaluated our method on osteoarthritis (OA) data from a multi-center longitudinal study (the Osteoarthritis Initiative, OAI). With presymptomatic baseline data, our model is generative and significantly outperforms the end-to-end learning model in discriminating the progressive cohort. Two experiments were performed with seven experienced radiologists. When no synthetic follow-up radiographs were provided, our model performed better than all seven radiologists. In cases where the synthetic follow-ups generated by our model were available, the specificity and sensitivity of all readers in discriminating progressors increased from 72.3% to 88.6% and from 42.1% to 51.6%, respectively. Our results open up a new possibility of using model-based morphology and risk prediction to make predictions about future disease occurrence, as demonstrated in the example of OA. © 2021, CC BY.

关键词： Extrapolation

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Why is the Winner the Best?

Why is the Winner the Best?

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： M. Eisenmann A. Reinke V. Weru M. D. Tizabi F. Isensee T. J. Adler S. Ali V. Andrearczyk M. Aubreville U. Baid S. Bakas N. Balu S. Bano J. Bernal S. Bodenstedt A. Casella V. Cheplygina M. Daum M. De Bruijne A. Depeursinge R. Dorent J. Egger D. G. Ellis S. Engelhardt M. Ganz N. Ghatwary G. Girard P. Godau A. Gupta L. Hansen K. Harada M. Heinrich N. Heller A. Hering A. Huaulmé P. Jannin A. E. Kavur O. Kodym M. Kozubek J. Li H. Li J. Ma C. Martín-Isla B. Menze A. Noble V. Oreiller N. Padoy S. Pati K. Payette T. Rädsch J. Rafael-Patiño V. Singh Bawa S. Speidel C. H. Sudre K. Van Wijnen M. Wagner D. Wei A. Yamlahi M. H. Yap C. Yuan M. Zenk A. Zia D. Zimmerer D. Aydogan B. Bhattarai L. Bloch R. Brüngel J. Cho C. Choi Q. Dou I. Ezhov C. M. Friedrich C. Fuller R. R. Gaire A. Galdran Á. García Faura M. Grammatikopoulou S. Hong M. Jahanifar I. Jang A. Kadkhodamohammadi I. Kang F. Kofler S. Kondo H. Kuijf M. Li M. Luu T. Martinčič P. Morais M. A. Naser B. Oliveira D. Owen S. Pang J. Park S. Park S. Płotka E. Puybareau N. Rajpoot K. Ryu N. Saeed A. Shephard P. Shi D. Štepec R. Subedi G. Tochon H. R. Torres H. Urien J. L. Vilaça K. A. Wahid H. Wang J. Wang L. Wang X. Wang B. Wiestler M. Wodzinski F. Xia J. Xie Z. Xiong S. Yang Y. Yang Z. Zhao K. Maier-Hein P. F. Jäger A. Kopp-Schneider L. Maier-Hein Division of Intelligent Medical Systems German Cancer Research Center (DKFZ) Heidelberg Germany Helmholtz Imaging German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Division of Biostatistics German Cancer Research Center (DKFZ) Heidelberg Germany Division of Medical Image Computing German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Engineering and Physical Sciences School of Computing University of Leeds Leeds UK Institute of Informatics School of Management HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland Sierre Switzerland Department of Nuclear Medicine and Molecular Imaging Lausanne University Hospital Lausanne Switzerland Technische Hochschule Ingolstadt Ingolstadt Germany Center for Artificial Intelligence and Data Science for Integrated Diagnostics (AI2D) and Center for Biomedical Image Computing and Analytics (CBICA) University of Pennsylvania Philadelphia PA USA Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Radiology Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Radiology University of Washington Seattle WA USA Department of Computer Science Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS) University College London London UK Universitat Autònoma de Barcelona & Computer Vision Center Barcelona Spain Division of Translational Surgical Oncology National Center for Tumor Diseases (NCT/UCC) Dresden Dresden Germany Department of Advanced Robotics Istituto Italiano di Tecnologia Italy Department of Electronics Information and Bioengineering Politecnico di Milano Milan Italy IT University of Copenhagen Copenhagen Denmark Department of General Visceral and Transplantation Surgery Heidelberg University Hospital Heidelberg Germany Department of Radiology and Nuc

International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really generate scientific progress? What are common and successful participation strategies? What makes a solution superior to a competing method? To address this gap in the literature, we performed a multicenter study with all 80 competitions that were conducted in the scope of IEEE ISBI 2021 and MICCAI 2021. Statistical analyses performed based on comprehensive descriptions of the submitted algorithms linked to their rank as well as the underlying participation strategies revealed common characteristics of winning solutions. These typically include the use of multi-task learning (63%) and/or multi-stage pipelines (61%), and a focus on augmentation (100%), image preprocessing (97%), data curation (79%), and post-processing (66%). The “typical” lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning. Two core general development strategies stood out for highly-ranked teams: the reflection of the metrics in the method design and the focus on analyzing and handling failure cases. According to the organizers, 43% of the winning algorithms exceeded the state of the art but only 11% completely solved the respective domain problem. The insights of our study could help researchers (1) improve algorithm development strategies when approaching new problems, and (2) focus on open research questions revealed by this work.

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Erratum to 'Benchmarking weakly-supervised deep learning pipelines for whole slide classification in computational pathology' medical Image Analysis, Volume 79, July 2022, 102474

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medical image analysis 2022年 82卷 102622页

作者： Narmin Ghaffari Laleh Hannah Sophie Muti Chiara Maria Lavinia Loeffler Amelie Echle Oliver Lester Saldanha Faisal Mahmood Ming Y Lu Christian Trautwein Rupert Langer Bastian Dislich Roman D Buelow Heike Irmgard Grabsch Hermann Brenner Jenny Chang-Claude Elizabeth Alwers Titus J Brinker Firas Khader Daniel Truhn Nadine T Gaisa Peter Boor Michael Hoffmeister Volkmar Schulz Jakob Nikolas Kather Department of Medicine III University Hospital RWTH Aachen Aachen Germany. Department of Pathology Brigham and Women's Hospital Harvard Medical School Boston MA USA. Institute of Pathology and Molecular Pathology Kepler University Hospital Johannes Kepler University Linz Linz Austria. Institute of Pathology University of Bern Switzerland. Institute of Pathology University Hospital RWTH Aachen Aachen Germany. Department of Pathology GROW School for Oncology and Developmental Biology Maastricht University Medical Center+ Maastricht The Netherlands. Division of Pathology and Data Analytics Leeds Institute of Medical Research at St James's University of Leeds Leeds UK. Division of Clinical Epidemiology and Aging Research German Cancer Research Center (DKFZ) Heidelberg Germany Division of Preventive Oncology German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT) Heidelberg Germany German Cancer Consortium (DKTK) German Cancer Research Center (DKFZ) Heidelberg Germany. Division of Cancer Epidemiology German Cancer Research Center (DKFZ) Heidelberg Germany Cancer Epidemiology Group University Cancer Center Hamburg University Medical Center Hamburg-Eppendorf Hamburg Germany. Division of Clinical Epidemiology and Aging Research German Cancer Research Center (DKFZ) Heidelberg Germany. Digital Biomarkers for Oncology Group German Cancer Research Center (DKFZ) Heidelberg Germany. Department of Radiology University Hospital RWTH Aachen Aachen Germany. Department of Physics of Molecular Imaging Systems Experimental Molecular Imaging RWTH Aachen University Aachen Germany Fraunhofer Institute for Digital Medicine MEVIS Bremen Germany Comprehensive Diagnostic Center Aachen (CDCA) University Hospital Aachen Aachen Germany Hyperion Hybrid Imaging Systems GmbH Aachen Germany. Department of Medicine III University Hospital RWTH Aachen Aachen Germany Division of Pathology and Data Analytics Leeds Institute of Medical Research at St James's Uni

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The Brain Tumor Sequence Registration (BraTS-Reg) Challenge: Establishing Correspondence Between Pre-Operative and Follow-up MRI Scans of Diffuse Glioma Patients

arXiv

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arXiv 2021年

作者： Baheti, Bhakti Chakrabarty, Satrajit Akbari, Hamed Bilello, Michel Wiestler, Benedikt Schwarting, Julian Calabrese, Evan Rudie, Jeffrey Abidi, Syed Mousa, Mina Villanueva-Meyer, Javier Fields, Brandon K.K. Kofler, Florian Shinohara, Russell Takeshi Iglesias, Juan Eugenio Mok, Tony C.W. Chung, Albert C.S. Wodzinski, Marek Jurgas, Artur Marini, Niccolò Atzori, Manfredo Müller, Henning Grosbröhmer, Christoph Siebert, Hanna Hansen, Lasse Heinrich, Mattias P. Canalini, Luca Klein, Jan Gerken, Annika Heldmann, Stefan Hering, Alessa Hahn, Horst K. Meng, Mingyuan Bi, Lei Feng, Dagan Kim, Jinman Zeineldinal, Ramy A. Karar, Mohamed E. Mathis-Ullrich, Franziska Burgert, Oliver Abderezaei, Javid Pionteck, Aymeric Chopra, Agamdeep Kurt, Mehmet Yan, Kewei Yan, Yonghong Tang, Zhe Ma, Jianqiang Nasser, Sahar Almahfouz Kurian, Nikhil Cherian Meena, Mohit Shamsi, Saqib Sethi, Amit Tustison, Nicholas J. Avants, Brian B. Cook, Philip Gee, James C. Tian, Lin Greer, Hastings Niethammer, Marc Hoopesar, Andrew Hoffmannas, Malte Dalcaar, Adrian V. Christodoulidis, Stergios Estiene, Théo Vakalopoulou, Maria Paragios, Nikos Marcus, Daniel S. Davatzikos, Christos Sotiras, Aristeidis Menze, Bjoern Bakas, Spyridon Waldmannstetter, Diana Division of Computational Pathology Department of Pathology and Laboratory Medicine Indiana University School of Medicine IndianapolisIN United States Department of Electrical and Systems Engineering Washington University in St. Louis St. LouisMO United States GE HealthCare San RamonCA United States University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Mallinckrodt Institute of Radiology Washington University School of Medicine St. LouisMO United States Institute for Informatics Data Science & Biostatistics Washington University School of Medicine St. LouisMO United States Department of Bioengineering Santa Clara University Santa ClaraCA United States Department of Quantitative Biomedicine University of Zurich Zurich Switzerland Department of Computer Science TUM School of Computation Information and Technology Technical University of Munich Munich Germany Department of Diagnostic and Interventional Neuroradiology School of Medicine Klinikum rechts der Isar Technical University of Munich Munich Germany Department of Nuclear Medicine University Hospital Regensburg Regensburg Germany University Hospital Regensburg Regensburg Germany Department of Radiology & Biomedical Imaging University of California San FranciscoCA United States Department of Radiology Duke University Medical Center DurhamNC United States Department of Radiology University of California San DiegoCA United States Helmholtz AI Helmholtz Munich Neuherberg Germany Penn Statistics in Imaging and Visualization Center Department of Biostatistics Epidemiology and Informatics University of Pennsylvania Philadelphia United States Massachusetts General Hospital Harvard Medical School United States Centre for Medical Imaging Computing University College London United Kingdom The Hong Kong University of Science and Technology Hong Kong University of Applied Sciences West

Registration of longitudinal brain Magnetic Resonance Imaging (MRI) scans containing pathologies is challenging due to dramatic changes in tissue appearance. Although there has been considerable progress in developing general-purpose medical image registration techniques, they have not yet attained the requisite precision and reliability for this task, highlighting its inherent complexity. Here we describe the Brain Tumor Sequence Registration (BraTS-Reg) challenge, as the first public benchmark environment for deformable registration algorithms focusing on estimating correspondences between pre-operative and follow-up scans of the same patient diagnosed with a diffuse brain glioma. The challenge was conducted in conjunction with both the IEEE International Symposium on Biomedical Imaging (ISBI) 2022 and the International Conference on medical Image Computing and Computer-Assisted Intervention (MICCAI) 2022. The BraTS-Reg data comprise de-identified multi-institutional multi-parametric MRI (mpMRI) scans, curated for size and resolution according to a canonical anatomical template, and divided into training, validation, and testing sets. Clinical experts annotated ground truth (GT) landmark points of anatomical locations distinct across the temporal domain. The training data with their GT annotations, were publicly released to enable the development of registration algorithms. The validation data, without their GT annotations, were also released to allow for algorithmic evaluation prior to the testing phase, which only allowed submission of containerized algorithms for evaluation on hidden hold-out testing data. Quantitative evaluation and ranking was based on the Median Euclidean Error (MEE), Robustness, and the determinant of the Jacobian of the displacement field. The top-ranked methodologies yielded similar performance across all evaluation metrics and shared several methodological commonalities, including pre-alignment, deep neural networks, inverse consistency

关键词： Magnetic resonance imaging

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Biomedical image analysis competitions: The state of current participation practice

arXiv

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arXiv 2022年

作者： Eisenmann, Matthias Reinke, Annika Weru, Vivienn Tizabi, Minu Dietlinde Isensee, Fabian Adler, Tim J. Godau, Patrick Cheplygina, Veronika Kozubek, Michal Maier-Hein, Klaus Jäger, Paul F. Kopp-Schneider, Annette Maier-Hein, Lena Ali, Sharib Gupta, Anubha Kybic, Jan Noble, Alison de Solórzano, Carlos Ortiz Pachade, Samiksha Petitjean, Caroline Sage, Daniel Wei, Donglai Wilden, Elizabeth Alapatt, Deepak Andrearczyk, Vincent Baid, Ujjwal Bakas, Spyridon Balu, Niranjan Bano, Sophia Bawa, Vivek Singh Bernal, Jorge Bodenstedt, Sebastian Casella, Alessandro Choi, Jinwook Commowick, Olivier Daum, Marie Depeursinge, Adrien Dorent, Reuben Egger, Jan Eichhorn, Hannah Engelhardt, Sandy Ganz, Melanie Girard, Gabriel Hansen, Lasse Heinrich, Mattias Heller, Nicholas Hering, Alessa Huaulmé, Arnaud Kim, Hyunjeong Li, Hongwei Bran Landman, Bennett Li, Jianning Ma, Jun Martel, Anne Martín-Isla, Carlos Menze, Bjoern Nwoye, Chinedu Innocent Oreiller, Valentin Padoy, Nicolas Pati, Sarthak Payette, Kelly Sudre, Carole van Wijnen, Kimberlin Vardazaryan, Armine Vercauteren, Tom Wagner, Martin Wang, Chuanbo Yap, Moi Hoon Yu, Zeyun Yuan, Chun Zenk, Maximilian Zia, Aneeq Zimmerer, David Bao, Rina Choi, Chanyeol Cohen, Andrew Dzyubachyk, Oleh Galdran, Adrian Gan, Tianyuan Guo, Tianqi Gupta, Pradyumna Haithami, Mahmood Ho, Edward Jang, Ikbeom Li, Zhili Luo, Zhengbo Lux, Filip Makrogiannis, Sokratis Müller, Dominik Oh, Young-Tack Pang, Subeen Pape, Constantin Polat, Gorkem Reed, Charlotte Rosalie Ryu, Kanghyun Scherr, Tim Thambawita, Vajira Wang, Haoyu Wang, Xinliang Xu, Kele Yeh, Hung Yeo, Doyeob Yuan, Yixuan Zeng, Yan Zhao, Xin Abbing, Julian Adam, Jannes Adluru, Nagesh Agethen, Niklas Ahmed, Salman Al Khalil, Yasmina Alenyà, Mireia Alhoniemi, Esa An, Chengyang Arega, Tewodros Weldebirhan Avisdris, Netanell Aydogan, Dogu Baran Bai, Yingbin Calisto, Maria Baldeon Basaran, Berke Doga Beetz, Marcel Bian, Hao Blansit, Kevin Bloch, Louise Bohnsack, Robert Bosticardo, Sara Breen, Jack Brudfors, Mikael Brüngel, Raphael Cabezas, Mariano Cacciola, Alb Heidelberg Division of Intelligent Medical Systems Germany Heidelberg HI Helmholtz Imaging Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Heidelberg Division of Biostatistics Germany Heidelberg Division of Medical Image Computing Germany Heidelberg HI Applied Vision Lab Germany IT University of Copenhagen Copenhagen Denmark Centre for Biomedical Image Analysis Masaryk University Brno Czech Republic Heidelberg Interactive Machine Learning Group Germany Faculty of Mathematics and Computer Science and Medical Faculty Heidelberg University Heidelberg Germany NCT Heidelberg DKFZ University Hospital Heidelberg Germany School of Computing University of Leeds Leeds United Kingdom SBILab Department of ECE IIIT-Delhi India Faculty of Electrical Engineering Czech Technical University Prague Czech Republic Institute of Biomedical Engineering University of Oxford United Kingdom Center for Applied Medical Research Pamplona Spain Shri Guru Gobind Singhji Institute of Engineering and Technology Maharashtra Nanded India Université de Rouen Normandie France Lausanne Switzerland School of Engineering and Applied Science Harvard University United States ICube University of Strasbourg CNRS France Institute of Informatics School of Management HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland Techno-Pôle 3 Sierre3960 Switzerland Department of Nuclear Medicine and Molecular Imaging Lausanne University Hospital Rue du Bugnon 46 LausanneCH-1011 Switzerland University of Pennsylvania PhiladelphiaPA United States Department of Radiology University of Washington United States Wellcome EPSRC Centre for Interventional and Surgical Sciences University College London London United Kingdom Visual Artificial Intelligence Lab Oxford Brookes University Oxford United Kingdom Universitat Autònoma de Barcelona & Computer Vision Center Spain Dresden Fetscherstraße 74 PF 64 Dresden01307 Germany

The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known about the common practice as well as bottlenecks faced by the community in tackling the research questions posed. To shed light on the status quo of algorithm development in the specific field of biomedical imaging analysis, we designed an international survey that was issued to all participants of challenges conducted in conjunction with the IEEE ISBI 2021 and MICCAI 2021 conferences (80 competitions in total). The survey covered participants' expertise and working environments, their chosen strategies, as well as algorithm characteristics. A median of 72% challenge participants took part in the survey. According to our results, knowledge exchange was the primary incentive (70%) for participation, while the reception of prize money played only a minor role (16%). While a median of 80 working hours was spent on method development, a large portion of participants stated that they did not have enough time for method development (32%). 25% perceived the infrastructure to be a bottleneck. Overall, 94% of all solutions were deep learning-based. Of these, 84% were based on standard architectures. 43% of the respondents reported that the data samples (e.g., images) were too large to be processed at once. This was most commonly addressed by patch-based training (69%), downsampling (37%), and solving 3D analysis tasks as a series of 2D tasks. K-fold cross-validation on the training set was performed by only 37% of the participants and only 50% of the participants performed ensembling based on multiple identical models (61%) or heterogeneous models (39%). 48% of the respondents applied postprocessing steps. © 2022, CC BY.

关键词： Benchmarking

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The Liver Tumor Segmentation Benchmark (LiTS)

arXiv

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arXiv 2019年

作者： Bilic, Patrick Christ, Patrick Li, Hongwei Bran Vorontsov, Eugene Ben-Cohen, Avi Kaissis, Georgios Szeskin, Adi Jacobs, Colin Mamani, Gabriel Efrain Humpire Chartrand, Gabriel Lohöfer, Fabian Holch, Julian Walter Sommer, Wieland Hofmann, Felix Hostettler, Alexandre Lev-Cohain, Naama Drozdzal, Michal Amitai, Michal Marianne Vivanti, Refael Sosna, Jacob Ezhov, Ivan Sekuboyina, Anjany Navarro, Fernando Kofler, Florian Paetzold, Johannes C. Shit, Suprosanna Hu, Xiaobin Lipková, Jana Rempfler, Markus Piraud, Marie Kirschke, Jan Wiestler, Benedikt Zhang, Zhiheng Hülsemeyer, Christian Beetz, Marcel Ettlinger, Florian Antonelli, Michela Bae, Woong Bellver, Míriam Bi, Lei Chen, Hao Chlebus, Grzegorz Dam, Erik B. Dou, Qi Fu, Chi-Wing Georgescu, Bogdan Giró-I-Nieto, Xavier Gruen, Felix Han, Xu Heng, Pheng-Ann Hesser, Jürgen Moltz, Jan Hendrik Igel, Christian Isensee, Fabian Jäger, Paul Jia, Fucang Kaluva, Krishna Chaitanya Khened, Mahendra Kim, Ildoo Kim, Jae-Hun Kim, Sungwoong Kohl, Simon Konopczynski, Tomasz Kori, Avinash Krishnamurthi, Ganapathy Li, Fan Li, Hongchao Li, Junbo Li, Xiaomeng Lowengrub, John Ma, Jun Maier-Hein, Klaus Maninis, Kevis-Kokitsi Meine, Hans Merhof, Dorit Pai, Akshay Perslev, Mathias Petersen, Jens Pont-Tuset, Jordi Qi, Jin Qi, Xiaojuan Rippel, Oliver Roth, Karsten Sarasua, Ignacio Schenk, Andrea Shen, Zengming Torres, Jordi Wachinger, Christian Wang, Chunliang Weninger, Leon Wu, Jianrong Xu, Daguang Yang, Xiaoping Yu, Simon Chun-Ho Yuan, Yading Yue, Miao Zhang, Liping Cardoso, Jorge Bakas, Spyridon Braren, Rickmer Heinemann, Volker Pal, Christopher Tang, An Kadoury, Samuel Soler, Luc van Ginneken, Bram Greenspan, Hayit Joskowicz, Leo Menze, Bjoern Department of Informatics Technical University of Munich Germany Department of Quantitative Biomedicine University of Zurich Switzerland Ecole Polytechnique de Montréal Canada Department of Medical Imaging Radboud University Medical Center Nijmegen Netherlands Department of Biomedical Engineering Tel-Aviv University Israel Germany Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Heidelberg Germany Philips Research China Philips China Innovation Campus Shanghai China School of Biomedical Engineering & Imaging Sciences King0s College London London United Kingdom Institute for AI in Medicine Technical University of Munich Germany Department of Computer Science Guangdong University of Foreign Studies China Institute for diagnostic and interventional radiology Klinikum rechts der Isar Technical University of Munich Germany Institute for Diagnostic and Interventional Neuroradiology Klinikum Rechts der Isar Technical University of Munich Germany Department of Hepatobiliary Surgery The Affiliated Drum Tower Hospital Nanjing University Medical School China Department of Computing Imperial College London London United Kingdom Institute for Tissue Engineering and Regenerative Medicine Helmholtz Zentrum München Neuherberg Germany Brigham and Women's Hospital Harvard Medical School United States School of Computer Science and Engineering The Hebrew University of Jerusalem Israel University of Pennsylvania PA United States Pte. Ltd. Singapore Medical Imaging and Reconstruction Lab Department of Engineering Design Indian Institute of Technology Madras India Sensetime Shanghai China Department of Radiology Perelman School of Medicine University of Pennsylvania United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PA United States Co. Ltd China MontréalQC Canada Department of Radiology Radiation Oncology and Nuclear Medicine University of Montréal

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://***/. In addition, both data and online evaluation are accessible via ***. © 2019, CC BY-NC-ND.

关键词： Deep learning

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Risk of venous congestion in live donors of extended right liver graft

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World Journal of Gastroenterology 2015年第19期21卷 6008-6017页

作者： Arnold Radtke George Sgourakis Ernesto P Molmenti Susanne Beckebaum Vito R Cicinnati Hartmut Schmidt Heinz-Otto Peitgen Christoph E Broelsch Massimo Malagó Tobias Schroeder Department of General and Transplantation Surgery University Hospital Tuebingen Department of General Surgery of Red Cross Hospital Department of Surgery North Shore University Hospital Manhasset Department of General Visceral and Transplantation SurgeryUniversity Hospital Essen Department of Transplant Medicine University Hospital Muenster MeVis Center for Medical Diagnostic Systems and Visualization University of Bremen Department of Surgery-UCL Division of Surgical and Interventional Sciences University College London Department of Diagnostic and Interventional Radiology University Hospital Essen

AIM: To investigate middle hepatic vein(MHV)management in adult living donor liver transplantation and safer remnant volumes(RV).METHODS: There were 59 grafts with and 12 grafts without MHV(including 4 with MHV-5/8 reconstructions).All donors underwent our five-step protocol evaluation containing a preoperative protocol liver biopsy Congestive vs non-congestive RV, remnantvolumebody-weight ratios(RVBWR) and postoperative outcomes were evaluated in 71 right graft living donors. Dominant vs non-dominant MHV anatomy in total liver volume(d-MHV/TLV vs nd-MHV/TLV) was constellated with large/small congestion volumes(CVindex).Small for size(SFS) and non-SFS remnant considerations were based on standard cut-off- RVBWR and RV/TLV. Non-congestive RVBWR was based on non-congestive ***: MHV and non-MHV remnants showed no significant differences in RV, RV/TLV, RVBWR, total bilirubin, or INR. SFS-remnants with RV/TLV < 30%and non-SFS-remnants with RV/TLV ≥ 30% showedno significant differences either. RV and RVBWR for non-MHV(n = 59) and MHV-containing(n = 12)remnants were 550 ± 95 ml and 0.79 ± 0.1 ml vs568 ± 97 ml and 0.79 ± 0.13, respectively(P = 0.423 and P = 0.919. Mean left RV/TLV was 35.8% ± 3.9%.Non-MHV(n = 59) and MHV-containing(n = 12)remnants(34.1% ± 3% vs 36% ± 4% respectively,P = 0.148. Eight SFS-remnants with RVBWR < 0.65 had a significantly smaller RV/TLV than 63 non-SFSremnants with RVBWR ≥ 0.65 [SFS: RV/TLV 32.4%(range: 28%-35.7%) vs non-SFS: RV/TLV 36.2%(range: 26.1%-45.5%), P < 0.009. Six SFS-remnants with RV/TLV < 30% had significantly smaller RVBWR than 65 non-SFS-remnants with RV/TLV ≥ 30%(0.65(range: 0.6-0.7) vs 0.8(range: 0.6-1.27), P < ***(2.8%) donors developed reversible liver *** and RV/TLV were concordant in 25%-33%of SFS and in 92%-94% of non-SFS remnants. MHV management options including complete MHV vs MHV-4A selective retention were necessary in n = 12 vs n =2 remnants based on particularly risky congestive and non-congestive

关键词： Living donor liver transplantation Livervolume Remnant volume Small-for-size Small-forsizesyndrome

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5th International Symposium on Focused Ultrasound Abstracts

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JOURNAL OF THERAPEUTIC ULTRASOUND 2016年第SUPPL 1期4卷 1-1页

作者： [Anonymous] Rambam Health Care Campus Haifa Israel Stanford University Stanford CA USA University of Pennsylvania Philadelphia PA USA The Ohio State University Columbus OH USA University of Virginia Charlottesville VA USA Center for Nanomedicine/Wilmer Eye Institute Johns Hopkins University Baltimore MD USA Columbia University New York NY USA Sunnybrook Health Sciences Centre Toronto ON Canada Sunnybrook Research Institute Toronto ON Canada Clem Jones Centre for Ageing Dementia Research Queensland Brain Institute The University of Queensland St Lucia QLD Australia The University of Queensland Brisbane (St Lucia Campus) QLD Australia University of Leuven Leuven Belgium University of Maryland School of Medicine/Baltimore VAMC Baltimore MD USA Research & Development Service VA Maryland Healthcare System and University of Maryland School of Medicine Baltimore MD USA University of Maryland School of Medicine Baltimore MD USA Brigham and Women’s Hospital Boston MA USA Harvard Medical School Boston MA USA Boston University Boston MA USA National Institutes of Health Bethesda MD USA National Institutes of Health Clinical Center Bethesda MD USA University of Utah Salt Lake City UT USA Focused Ultrasound Foundation Charlottesville VA USA Vanderbilt University Nashville TN USA Georgia Institute of Technology Atlanta GA USA Cleveland Clinic Cleveland OH USA Institut Langevin Ondes et Images ESPCI ParisTech CNRS UMR 7587 INSERM U979 Paris France CENIR ICM CNRS U7225 INSERM U975 Paris France University Children’s Hospital Zurich Zurich Switzerland Jeju National University Jeju Republic of Korea Hospital for Sick Children Toronto ON Canada Centre for Image Guided Innovation and Therapeutic Intervention Toronto ON Canada Philips Healthcare Canada Toronto ON Canada Toronto Western Hospital Toronto ON Canada Thunder Bay Regional Research Institute Thunder Bay ON Canada Incheon St. Mary’s Hospital The Catholic University of Korea Incheon Republic of Korea Departme

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Fast estimation of the vascular cooling in RFA based on numerical simulation

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Open Biomedical Engineering Journal 2010年第SPEC. ISSUE 1期4卷 16-26页

作者： Kröger, T. Pätz, T. Altrogge, I. Schenk, A. Lehmann, K.S. Frericks, B.B. Ritz, J.-P. Peitgen, H.-O. Preusser, T. Fraunhofer MEVIS Institute for Medical Image Computing Bremen Germany CeVis Center of Complex Systems and Visualization University of Bremen Germany Chirurgische Klinik I Charité - Campus Benjamin Franklin Berlin Germany Jacobs University Bremen Germany

We present a novel technique to predict the outcome of an RF ablation, including the vascular cooling effect. The main idea is to separate the problem into a patient independent part, which has to be performed only once for every applicator model and generator setting, and a patient dependent part, which can be performed very fast. The patient independent part fills a look-up table of the cooling effects of blood vessels, depending on the vessel radius and the distance of the RF applicator from the vessel, using a numerical simulation of the ablation process. The patient dependent part, on the other hand, only consists of a number of table look-up processes. The paper presents this main idea, along with the required steps for its implementation. First results of the computation and the related ex-vivo evaluation are presented and discussed. The paper concludes with future extensions and improvements of the approach. © Kröger et al.

关键词： Cooling

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Improving lesion detectability of a PEM system with post-reconstruction filtering

Improving lesion detectability of a PEM system with post-rec...

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IEEE Symposium on Nuclear Science (NSS/MIC)

作者： Weidong Luo Markus T. Harz Christopher G. Matthews Naviscan Inc. San Diego CA USA Institute for Medical Image Computing MeVis Center for Medical Diagnostic Systems and Visualization Bremen Germany Sinogram Solutions Knoxville TN USA

We present a method to quantify the image quality of a positron emission mammography (PEM) imaging system through the metric of lesion detectability. For a customized image quality phantom, we assess the impact of different post-reconstruction filters on the acquired PEM image. We acquired six image quality phantom images on a Naviscan PEM scanner using different scan durations which gave differing amounts of background noise. The image quality phantom has dimensions of 130 mm × 130 mm × 66 mm and consists of 15 hot rod inserts with diameters of approximately 10 mm, 5 mm, 4 mm, 3 mm, and 2 mm filled with activity ratios of 3.5, 6.8 and 12.7 times the background activity. One region of the phantom had no inserts so as to measure the uniformity of the background noise. Lesion detectability was determined for each background uniformity and each activity ratio by extrapolating a fit of the recovery coefficients to the point where the lesion would be lost in the noise of the background (defined as 3 times the background's standard deviation). The data were reconstructed by the system's standard clinical software using an MLEM algorithm with 5 iterations. We compare the lesion detectability of an unfiltered image to the image after applying one of five common post-reconstruction filters. Two of the filters were found to improve lesion detectability: a bilateral filter (9% improvement) and a Perona-Malik filter (8% improvement). One filter was found to have negligible effect: a Gaussian filter showed a 1% decrease in lesion detectability. The other two filters tested were found to worsen lesion detectability: a median filter (8% decrease) and a Stick filter (7% decrease).

关键词： Lesions Filters Image quality Imaging phantoms Background noise Filtering Radioactive decay Mammography Noise measurement Signal to noise ratio

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