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SHR Neuro Cancer Cardio Lipid Metab Microb

Ma, J; Zhang, Y; Gu, S; An, XL; Wang, ZH; Ge, C; Wang, CC; Zhang, F; Wang, Y; Xu, YA; Gou, SP; Thaler, F; Payer, C; Stern, D; Henderson, EGA; McSweeney, DM; Green, A; Jackson, P; McIntosh, L; Nguyen, QC; Qayyum, A; Conze, PH; Huang, ZY; Zhou, ZQ; Fan, DP; Xiong, H; Dong, GQ; Zhu, QJ; He, J; Yang, XP.
Fast and Low-GPU-memory abdomen CT organ segmentation: The FLARE challenge
MED IMAGE ANAL. 2022; 82: 102616 Doi: 10.1016/j.media.2022.102616
Web of Science PubMed FullText FullText_MUG

Co-authors Med Uni Graz: Stern Darko; Thaler Franz
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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 19x 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/.
Find related publications in this database (Keywords): Segmentation; Abdominal organ; Efficiency; Multi-center