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

Jun Ma, Department of Mathematics, Nanjing University of Science and Technology, Nanjing, 210094, China
Yao Zhang, Institute of Computing Technology, Chinese Academy of Sciences and the University of Chinese Academy of Sciences, Beijing, 100019, China
Song Gu, Department of Image Reconstruction, Nanjing Anke Medical Technology Co., Ltd., Nanjing, 211113, China
Xingle An, Infervision Technology Co. Ltd., Beijing, 100020, China
Zhihe Wang, Shenzhen Haichuang Medical Co., Ltd., Shenzhen, 518049, China
Cheng Ge, Institute of Bioinformatics and Medical Engineering, Jiangsu University of Technology, Changzhou, 213001, China
Congcong Wang, School of Computer Science and Engineering, Tianjin University of Technology, Tianjin, 300384, China & Engineering Research Center of Learning-Based Intelligent System, Ministry of Education, Tianjin, 300384, China
Fan Zhang, Radiological Algorithm, Fosun Aitrox Information Technology Co., Ltd., Shanghai, 200033, China
Yu Wang, Radiological Algorithm, Fosun Aitrox Information Technology Co., Ltd., Shanghai, 200033, China
Yinan Xu, Key Lab of Intelligent Perception and Image Understanding of Ministry of Education, Xidian University, Shaanxi, 710071, China
Shuiping Gou, Key Lab of Intelligent Perception and Image Understanding of Ministry of Education, Xidian University, Shaanxi, 710071, China
Franz Thaler, Gottfried Schatz Research Center: Biophysics, Medical University of Graz, 8010, Graz, Austria & Institute of Computer Graphics and Vision, Graz University of Technology, Graz, 8010, Austria
Christian Payer, Institute of Computer Graphics and Vision, Graz University of Technology, Graz, 8010, Austria
Darko Štern, Gottfried Schatz Research Center: Biophysics, Medical University of Graz, 8010, Graz, Austria
Edward G.A. Henderson, Division of Cancer Sciences, The University of Manchester, Manchester, M139PL, United Kingdom & Radiotherapy Related Research, The Christie NHS Foundation Trust, Manchester, M139PL, United Kingdom
Dónal M. McSweeney, Division of Cancer Sciences, The University of Manchester, Manchester, M139PL, United Kingdom & Radiotherapy Related Research, The Christie NHS Foundation Trust, Manchester, M139PL, United Kingdom
Andrew Green, Division of Cancer Sciences, The University of Manchester, Manchester, M139PL, United Kingdom & Radiotherapy Related Research, The Christie NHS Foundation Trust, Manchester, M139PL, United Kingdom
Price Jackson, Peter MacCallum Cancer Centre, Melbourne, 3000, Australia
Lachlan McIntosh, Peter MacCallum Cancer Centre, Melbourne, 3000, Australia
Quoc-Cuong Nguyen, University of Information Technology, VNU-HCM, Ho Chi Minh City, 700000, Vietnam
Abdul Qayyum, Brest National School of Engineering, UMR CNRS 6285 LabSTICC, Brest, 29280, France
Pierre-Henri Conze, IMT Atlantique, LaTIM UMR 1101, Inserm, Brest, 29238, France
Ziyan Huang, Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, 200240, China
Ziqi Zhou, Guangdong Key Laboratory of Intelligent Information Processing, Shenzhen University, Shenzhen, 518000, China
Deng-Ping Fan, College of Computer Science, Nankai University, Tianjin, 300071, China & Inception Institute of Artificial Intelligence, Abu Dhabi, United Arab Emirates
Huan Xiong, Mohamed bin Zayed University of Artificial Intelligence & Harbin Institute of Technology, Harbin, 150001, ChinaFollow
Guoqiang Dong, Department of Nuclear Medicine, Nanjing Drum Tower Hospital, the Affiliated Hospital of Nanjing University Medical School, Nanjing, 210008, China & Department of Interventional Radiology, The Second Affiliated Hospital of Bengbu Medical College, Bengbu, 233017, China
Qiongjie Zhu, Department of Nuclear Medicine, Nanjing Drum Tower Hospital, the Affiliated Hospital of Nanjing University Medical School, Nanjing, 210008, China & Department of Radiology, Shidong Hospital, Shanghai, 200438, China
Jian He, Department of Nuclear Medicine, Nanjing Drum Tower Hospital, the Affiliated Hospital of Nanjing University Medical School, Nanjing, 210008, China
Xiaoping Yang, Department of Mathematics, Nanjing University, Nanjing, 210093, China

Document Type

Article

Publication Title

Medical Image Analysis

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/. © 2022 Elsevier B.V.

DOI

10.1016/j.media.2022.102616

Publication Date

11-2022

Keywords

Abdominal organ, Efficiency, Multi-center, Segmentation, Computerized tomography, Graphics processing unit, Hospitals

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OA version (pathway a) Accepted version

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License: CC-by-NC-ND

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