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dc.contributor.authorMa, J.
dc.contributor.authorZhang, Y.
dc.contributor.authorGu, S.
dc.contributor.authorAn, X.
dc.contributor.authorWang, Z.
dc.contributor.authorGe, C.
dc.contributor.authorWang, C.
dc.contributor.authorZhang, F.
dc.contributor.authorWang, Y.
dc.contributor.authorXu, Y.
dc.contributor.authorGou, S.
dc.contributor.authorThaler, F.
dc.contributor.authorPayer, C.
dc.contributor.authorŠtern, D.
dc.contributor.authorHenderson, Edward G A
dc.contributor.authorMcSweeney, Donal M
dc.contributor.authorGreen, Andrew
dc.contributor.authorJackson, P.
dc.contributor.authorMcIntosh, L.
dc.contributor.authorNguyen, Q. C.
dc.contributor.authorQayyum, A.
dc.contributor.authorConze, P. H.
dc.contributor.authorHuang, Z.
dc.contributor.authorZhou, Z.
dc.contributor.authorFan, D. P.
dc.contributor.authorXiong, H.
dc.contributor.authorDong, G.
dc.contributor.authorZhu, Q.
dc.contributor.authorHe, J.
dc.contributor.authorYang, X.
dc.date.accessioned2022-10-26T12:58:51Z
dc.date.available2022-10-26T12:58:51Z
dc.date.issued2022en
dc.identifier.citationMa J, Zhang Y, Gu S, An X, Wang Z, Ge C, et al. Fast and Low-GPU-memory abdomen CT organ segmentation: The FLARE challenge. Medical image analysis. 2022 Sep 13;82:102616. PubMed PMID: 36179380. Epub 2022/10/01. eng.en
dc.identifier.pmid36179380en
dc.identifier.doi10.1016/j.media.2022.102616en
dc.identifier.urihttp://hdl.handle.net/10541/625696
dc.description.abstractAutomatic 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 19× 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/.en
dc.language.isoenen
dc.relation.urlhttps://dx.doi.org/10.1016/j.media.2022.102616en
dc.titleFast and Low-GPU-memory abdomen CT organ segmentation: The FLARE challengeen
dc.typeArticleen
dc.contributor.departmentDepartment of Mathematics, Nanjing University of Science and Technology, 210094, Nanjing, Chinaen
dc.identifier.journalMedical Image Analysisen
dc.description.noteen]


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