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

Jun Ma, Yao Zhang, Song Gu, Xingle An, Zhihe Wang, Cheng Ge, Congcong Wang, Fan Zhang, Yu Wang, Yinan Xu, Shuiping Gou, Franz Thaler, Christian Payer, Darko Štern, Edward G.A. Henderson, Dónal M. McSweeney, Andrew Green, Price Jackson, Lachlan McIntosh, Quoc Cuong NguyenAbdul Qayyum, Pierre Henri Conze, Ziyan Huang, Ziqi Zhou, Deng Ping Fan, Huan Xiong, Guoqiang Dong, Qiongjie Zhu, Jian He, Xiaoping Yang*

*Corresponding author for this work

Research output: Contribution to journalShort surveypeer-review

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 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/.

Original languageEnglish
Article number102616
JournalMedical Image Analysis
Volume82
DOIs
Publication statusPublished - Nov 2022

Keywords

  • Abdominal organ
  • Efficiency
  • Multi-center
  • Segmentation

ASJC Scopus subject areas

  • Radiological and Ultrasound Technology
  • Radiology Nuclear Medicine and imaging
  • Computer Vision and Pattern Recognition
  • Health Informatics
  • Computer Graphics and Computer-Aided Design

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