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Fast and low-GPU-memory abdomen CT organ segmentation: The FLARE challenge

Jun Ma 1 Yao Zhang 2 Song Gu 3 Xingle An 4 Zhihe Wang 5 Cheng Ge 6 Congcong Wang 7, 8 Fan Zhang 9 Yu Wang 9 Yinan Xu 10 Shuiping Gou 10 Franz Thaler 11, 12 Christian Payer 12 Darko Štern 11 Edward Henderson 13, 14 Donal Mcsweeney 13, 14 Andrew Green 13, 14 Price Jackson 15 Lachlan Mcintosh 16 Quoc-Cuong Nguyen 17 Abdul Qayyum 18 Pierre-Henri Conze 19, 20 Ziyan Huang 21 Ziqi Zhout 22 Deng-Ping Fan 23, 24 Huan Xiong 24, 25 Guoqiang Dong 26 Qiongjie Zhu 26 Jian He 26 Xiaoping Yang 27 
Abstract : Automatic segmentation of abdominal organs in CT scans plays an important role in clinical practice. However, most existing benchmarks and datasets only focus on segmentation accuracy, while the model efficiency and its accuracy on the testing cases from different medical centers have not been evaluated. To comprehensively benchmark abdominal organ segmentation methods, we organized the first Fast and Low GPU memory Abdominal oRgan sEgmentation (FLARE) challenge, where the segmentation methods were encouraged to achieve high accuracy on the testing cases from different medical centers, fast inference speed, and low GPU memory consumption, simultaneously. The winning method surpassed the existing state-of-the-art method, achieving a faster inference speed and reducing the GPU memory consumption by 60% with comparable accuracy. We provide a summary of the top methods, make their code and Docker containers publicly available, and give practical suggestions on building accurate and efficient abdominal organ segmentation models. The FLARE challenge remains open for future submissions through a live platform for benchmarking further methodology developments at https://flare.grand-challenge.org/.
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https://hal-imt-atlantique.archives-ouvertes.fr/hal-03768412
Contributor : Pierre-Henri Conze Connect in order to contact the contributor
Submitted on : Saturday, September 3, 2022 - 5:29:09 PM
Last modification on : Wednesday, September 28, 2022 - 4:11:47 PM

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Jun Ma, Yao Zhang, Song Gu, Xingle An, Zhihe Wang, et al.. Fast and low-GPU-memory abdomen CT organ segmentation: The FLARE challenge. Medical Image Analysis, Elsevier, 2022, 82 (November), pp.102616. ⟨10.1016/j.media.2022.102616⟩. ⟨hal-03768412⟩

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