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*

*Korrespondierende/r Autor/-in für diese Arbeit

Publikation: Beitrag in einer FachzeitschriftKurzer AbrissBegutachtung

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/.

Originalspracheenglisch
Aufsatznummer102616
FachzeitschriftMedical Image Analysis
Jahrgang82
DOIs
PublikationsstatusVeröffentlicht - Nov. 2022

ASJC Scopus subject areas

  • Radiologie- und Ultraschalltechnik
  • Radiologie, Nuklearmedizin und Bildgebung
  • Maschinelles Sehen und Mustererkennung
  • Gesundheitsinformatik
  • Computergrafik und computergestütztes Design

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