Automated pancreatic segmentation and fat fraction evaluation based on a self-supervised transfer learning network.

Journal: Computers in biology and medicine
Published Date: Jan 17, 2024

Abstract

Accurate segmentation of the pancreas from abdominal computed tomography (CT) images is challenging but essential for the diagnosis and treatment of pancreatic disorders such as tumours and diabetes. In this study, a dataset with 229 sets of high-resolution CT images was generated and annotated. We proposed a novel 3D segmentation model named nnTransfer (nonisomorphic transfer learning) net, which employs generative model structure for self-supervision to facilitate the network's learning of image attributes from unlabelled data. The effectiveness for pancreas segmentation of nnTransfer was assessed using the Hausdorff distance (HD) and Dice similarity coefficient (DSC) on the dataset. Additionally, a histogram analysis with local thresholding was used to achieve automated whole-volume measurement of pancreatic fat (fat volume fraction, FVF). The proposed technique performed admirably on the dataset, with DSC: 0.937 ± 0.019 and HD: 2.655 ± 1.479. The mean pancreas volume and FVF of the pancreas were 91.95 ± 23.90 cm and 12.67 % ± 9.84 %, respectively. The nnTransfer functioned flawlessly and autonomously, facilitating the use of the FVF to evaluate pancreatic disease, particularly in patients with diabetes.

Authors

  • Gaofeng Zhang
    School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China; Department of Radiology, Changhai Hospital of Shanghai, Naval Medical University, Shanghai, 200433, China.
  • Qian Zhan
    Department of Radiology, Changhai Hospital of Shanghai, Naval Medical University, Shanghai, 200433, China.
  • Qingyu Gao
    School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China; Department of Radiology, Changhai Hospital of Shanghai, Naval Medical University, Shanghai, 200433, China.
  • Kuanzheng Mao
    School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China; Department of Radiology, Changhai Hospital of Shanghai, Naval Medical University, Shanghai, 200433, China.
  • Panpan Yang
    Department of Radiology, Changhai Hospital of Shanghai, Second Military Medical University, Shanghai 200433, China.
  • Yisha Gao
    Department of Pathology, Changhai Hospital of Shanghai, Naval Medical University, China.
  • Lijia Wang
    Department of Information Engineering and Automation, Hebei College of Industry and Technology, Shijiazhuang, China.
  • Bin Song
    Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, China.
  • Yufei Chen
    College of Electronics and Information Engineering, Tongji University, Shanghai 200092, China. Electronic address: yufeichen@tongji.edu.cn.
  • Yun Bian
    Department of Radiology, Changhai Hospital.
  • Chengwei Shao
    Department of Radiology, Changhai Hospital.
  • Jianping Lu
    Department of Radiology, Changhai Hospital.
  • Chao Ma

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