Learn2Reg: comprehensive multi-task medical image registration challenge, dataset and evaluation in the era of deep learning

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Learn2Reg: comprehensive multi-task medical image registration challenge, dataset and evaluation in the era of deep learning

Hering, Alessa (författare): Dept. of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen, GA, NL

Hansen, Lasse (författare): Institute of Medical Informatics, Universität zu Lübeck, Lübeck, DE, Germany

Mok, Tony C. W. (författare): Dept. of Computer Science and Engineering, The Hong Kong University of Science and Technology, HK, China

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Chung, Albert C. S. (författare): Dept. of Computer Science and Engineering, The Hong Kong University of Science and Technology, HK, China

Siebert, Hanna (författare): Institute of Medical Informatics, Universität zu Lübeck, Lübeck, DE, Germany

Hager, Stephanie (författare): Fraunhofer MEVIS, Institute for Digital Medicine, Lübeck, DE, Germany

Lange, Annkristin (författare): Fraunhofer MEVIS, Institute for Digital Medicine, Lübeck, DE, Germany

Kuckertz, Sven (författare): Fraunhofer MEVIS, Institute for Digital Medicine, Lübeck, DE, Germany

Heldmann, Stefan (författare): Fraunhofer MEVIS, Institute for Digital Medicine, Lübeck, DE, Germany

Shao, Wei (författare): Dept. of Radiology, Stanford University, Stanford, US

Vesal, Sulaiman (författare): Dept. of Urology, Stanford University, Stanford, US

Rusu, Mirabela (författare): Dept. of Radiology, Stanford University, Stanford, US

Sonn, Geoffrey (författare): Dept. of Urology, Stanford University, Stanford, US

Estienne, Theo (författare): Mathématiques et Informatique pour la Complexité et les Systèmes, Inria Saclay, Université Paris-Saclay, CentraleSupélec, Gif-sur-Yvette, FR

Vakalopoulou, Maria (författare): Mathématiques et Informatique pour la Complexité et les Systèmes, Inria Saclay, Université Paris-Saclay, CentraleSupélec, Gif-sur-Yvette, FR

Han, Luyi (författare): Dept. of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen, NL

Huang, Yunzhi (författare): School of Automation, Nanjing University of Information Science and Technology, Nanjing, CN

Yap, Pew-Thian (författare): Dept. of Radiology and Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, US

Brudfors, Mikael (författare): King’s College, London, UK

Balbastre, Yael (författare): Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, US

Joutard, Samuel (författare): King’s College, London, UK

Modat, Marc (författare): King’s College, London, UK

Lifshitz, Gal (författare): Tel Aviv University, IL, USA

Raviv, Dan (författare): Tel Aviv University, IL, USA

Lv, Jinxin (författare): Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, CN

Li, Qiang (författare): Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, CN

Jaouen, Vincent (författare): UMR 1101 LaTIM, IMT Atlantique, Inserm, Brest, FR

Visvikis, Dimitris (författare): UMR 1101 LaTIM, IMT Atlantique, Inserm, Brest, FR

Fourcade, Constance (författare): Ecole Centrale de Nantes, LS2N, UMR CNRS, Nantes, FR

Rubeaux, Mathieu (författare): Keosys Medical Imaging, Saint Herblain, FR

Pan, Wentao (författare): Shenzhen International Graduate School, Tsinghua University, CN

Xu, Zhe (författare): Dept. of Biomedical Engineering, The Chinese University of Hong Kong, HK, China

Jian, Bailiang (författare): Chair for Computer Aided Medical Procedures and Augmented Reality, TUM, Garching, DE, Germany

De Benetti, Francesca (författare): Chair for Computer Aided Medical Procedures and Augmented Reality, TUM, Garching, DE, Germany

Wodzinski, Marek (författare): Dept. of Measurement and Electronics, AGH University of Science and Technology, Krakow, PL

Gunnarsson, Niklas (författare): Uppsala universitet,Artificiell intelligens,Avdelningen för systemteknik

Sjölund, Jens, Biträdande lektor, 1987- (författare): Uppsala universitet,Avdelningen för systemteknik,Artificiell intelligens

Grzech, Daniel (författare): Dept. of Computing, Imperial College London, UK

Qiu, Huaqi (författare): Dept. of Computing, Imperial College London, UK

Li, Zeju (författare): Dept. of Computing, Imperial College London, UK

Thorley, Alexander (författare): University of Birmingham, UK

Duan, Jinming (författare): University of Birmingham, UK

Grossbrohmer, Christoph (författare): Institute of Medical Informatics, Universität zu Lübeck, Lübeck, DE, Germany

Hoopes, Andrew (författare): Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, US

Reinertsen, Ingerid (författare): Dept. Health Research, SINTEF Digital, Trondheim, NO

Xiao, Yiming (författare): Western University, London, CA

Landman, Bennett (författare): Dept. of Electrical and Computer Engineering, Vanderbilt University, Nashville, US

Huo, Yuankai (författare): Dept. of Electrical and Computer Engineering, Vanderbilt University, Nashville, US

Murphy, Keelin (författare): Dept. of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen, NL

Lessmann, Nikolas (författare): Dept. of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen, NL

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Dept of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen, GA, NL Institute of Medical Informatics, Universität zu Lübeck, Lübeck, DE, Germany (creator_code:org_t)

Institute of Electrical and Electronics Engineers (IEEE), 2023
2023
Engelska.
Ingår i: IEEE Transactions on Medical Imaging. - : Institute of Electrical and Electronics Engineers (IEEE). - 0278-0062 .- 1558-254X. ; 42:3, s. 697-712

Relaterad länk:: https://doi.org/10.1...; visa fler...; https://uu.diva-port... (primary) (Raw object); https://urn.kb.se/re...; https://doi.org/10.1...; visa färre...

Tidskriftsartikel (refereegranskat)

Abstract Ämnesord

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Image registration is a fundamental medical image analysis task, and a wide variety of approaches have been proposed. However, only a few studies have comprehensively compared medical image registration approaches on a wide range of clinically relevant tasks. This limits the development of registration methods, the adoption of research advances into practice, and a fair benchmark across competing approaches. The Learn2Reg challenge addresses these limitations by providing a multi-task medical image registration data set for comprehensive characterisation of deformable registration algorithms. A continuous evaluation will be possible at https:// learn2reg.grand-challenge.org. Learn2Reg covers a wide range of anatomies (brain, abdomen, and thorax), modalities (ultrasound, CT, MR), availability of annotations, as well as intra- and inter-patient registration evaluation. We established an easily accessible framework for training and validation of 3D registration methods, which enabled the compilation of results of over 65 individual method submissions from more than 20 unique teams. We used a complementary set of metrics, including robustness, accuracy, plausibility, and runtime, enabling unique insight into the current state-of-the-art of medical image registration. This paper describes datasets, tasks, evaluation methods and results of the challenge, as well as results of further analysis of transferability to new datasets, the importance of label supervision, and resulting bias. While no single approach worked best across all tasks, many methodological aspects could be identified that push the performance of medical image registration to new state-of-the-art performance. Furthermore, we demystified the common belief that conventional registration methods have to be much slower than deep-learning-based methods.

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Learn2Reg: comprehensive multi-task medical image registration challenge, dataset and evaluation in the era of deep learning

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