Unleashing the strengths of unlabelled data in deep learning-assisted pan-cancer abdominal organ quantification: the FLARE22 challenge.

Journal: The Lancet. Digital health
Published Date: Nov 1, 2024

Abstract

Deep learning has shown great potential to automate abdominal organ segmentation and quantification. However, most existing algorithms rely on expert annotations and do not have comprehensive evaluations in real-world multinational settings. To address these limitations, we organised the FLARE 2022 challenge to benchmark fast, low-resource, and accurate abdominal organ segmentation algorithms. We first constructed an intercontinental abdomen CT dataset from more than 50 clinical research groups. We then independently validated that deep learning algorithms achieved a median dice similarity coefficient (DSC) of 90·0% (IQR 87·4-91·3%) by use of 50 labelled images and 2000 unlabelled images, which can substantially reduce manual annotation costs. The best-performing algorithms successfully generalised to holdout external validation sets, achieving a median DSC of 89·4% (85·2-91·3%), 90·0% (84·3-93·0%), and 88·5% (80·9-91·9%) on North American, European, and Asian cohorts, respectively. These algorithms show the potential to use unlabelled data to boost performance and alleviate annotation shortages for modern artificial intelligence models.

Authors

  • Jun Ma
    State Key Laboratory of Urban Water Resource and Environment, Harbin Institute of Technology, Harbin 150090, China.
  • Yao Zhang
    Department of Nephrology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China.
  • Song Gu
    Department of Radiation Oncology, Hangzhou Yikang Chinese Medicine Oncology Hospital, Hangzhou, China.
  • Cheng Ge
    Institute of Bioinformatics and Medical Engineering, Jiangsu University of Technology, Changzhou, 213001, P. R. China.
  • Shihao Mae
    University Health Network, Toronto, ON, Canada; Department of Computer Science, University of Toronto, Toronto, ON, Canada; Vector Institute, Toronto, ON, Canada.
  • Adamo Young
    Vector Institute, Toronto, ON M5G 1M1, Canada.
  • Cheng Zhu
    Translational Sciences, Sanofi US, Framingham, MA, 01701, USA. Cheng.Zhu@sanofi.com.
  • Xin Yang
    Department of Oral Maxillofacial-Head Neck Oncology, Ninth People's Hospital, College of Stomatology, Shanghai Jiao Tong University School of Medicine, National Clinical Research Center for Oral Diseases, Shanghai Key Laboratory of Stomatology & Shanghai Research Institute of Stomatology, Shanghai, China.
  • Kangkang Meng
    University of Science and Technology Beijing, Beijing, China.
  • Ziyan Huang
    Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, China; Shanghai AI Laboratory, Shanghai, China.
  • Fan Zhang
    Department of Anesthesiology, Bishan Hospital of Chongqing Medical University, Chongqing, China.
  • Yuanke Pan
    Shenzhen Technology University, Shenzhen, China.
  • Shoujin Huang
    College of Big Data and Internet, Shenzhen Technology University, Shenzhen, China.
  • Jiacheng Wang
    Vanderbilt University, Nashville TN 37215, USA.
  • Mingze Sun
    The Institute of Intelligent Medical, Department of Biomedical Engineering, School of Control Science and Engineering, Shandong University, Jinan, China.
  • Rongguo Zhang
    Infervision, Beijing, China.
  • Dengqiang Jia
    School of Naval Architecture, Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai, China.
  • Jae Won Choi
  • Natália Alves
    Diagnostic Imaging Analysis Group, Medical Imaging Department, Radboud University Medical Center, Geert Grooteplein Zuid 10, 6525 GA, Nijmegen, the Netherlands.
  • Bram de Wilde
    From the Department of Radiology, Nuclear Medicine and Anatomy, Radboud University Medical Center, P.O. Box 9101, 6500 HB Nijmegen, the Netherlands (N.L., C.I.S., L.H.B., M.B., E.C., W.M.v.E., P.K.G., B.G., M.G., N.H., W.H., H.J.H., C.J., R.K., M.K., K.v.L., J.M., M.O., R.S., C. Schaefer-Prokop, S.S., E.T.S., C. Sital, J.T., K.V.V., C.d.V., W.X., B.d.W., M.P., B.v.G.); Department of Radiology, Academic Medical Center, Amsterdam, the Netherlands (L.B.); Thirona, Nijmegen, the Netherlands (J.P.C., E.M.v.R.); Departments of Internal Medicine (T.D.) and Radiology (M.V.), Canisius-Wilhelmina Ziekenhuis, Nijmegen, the Netherlands; Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, Maastricht, the Netherlands (H.A.G.); GROW School of Oncology and Developmental Biology, Maastricht, the Netherlands (H.A.G.); Departments of Biomedical Physics and Engineering and Radiology and Nuclear Medicine, Amsterdam University Medical Center, Amsterdam, the Netherlands (L.v.H., I.I.); Department of Radiology, Zuyderland Medical Center, Heerlen, the Netherlands (J.K.); Fraunhofer Institute for Digital Medicine MEVIS, Bremen, Germany (B.L.); Department of Radiology and Nuclear Medicine, Haaglanden Medical Center, The Hague, the Netherlands (T.v.R.V.); Department of Radiology, Meander Medical Center, Amersfoort, the Netherlands (C. Schaefer-Prokop, S.S.); and Department of Radiology, Leiden University Medical Center, Leiden, the Netherlands (J.L.S.).
  • Gregor Koehler
    Division of Medical Image Computing, German Cancer Research Center Heidelberg, Heidelberg, Germany; German Cancer Research Center Heidelberg, Heidelberg, Germany; Helmholtz Information and Data Science School for Health, Karlsruhe/Heidelberg, Germany.
  • Haoran Lai
    Southern Medical University, Guangzhou, China.
  • Ershuai Wang
    Shenzhen Yorktal DMIT, Shenzhen, China.
  • Manuel Wiesenfarth
    Division of Biostatistics, German Cancer Research Center, Im Neuenheimer Feld 581, 69210, Heidelberg, Germany.
  • Qiongjie Zhu
    Department of Radiology, Nanjing Drum Tower Hospital, the Affiliated Hospital of Nanjing University Medical School, Nanjing 210094, People's Republic of China.
  • Guoqiang Dong
    Department of Radiology, Nanjing Drum Tower Hospital, the Affiliated Hospital of Nanjing University Medical School, Nanjing 210094, People's Republic of China.
  • Jian He
    School of Software Engineering, Beijing University of Technology, Beijing, China. Electronic address: jianhee@bjut.edu.cn.
  • Bo Wang
    Department of Clinical Laboratory Medicine Center, Inner Mongolia Autonomous Region People's Hospital, Hohhot, Inner Mongolia, China.

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