Comparison of publicly available artificial intelligence models for pancreatic segmentation on T1-weighted Dixon images.

Journal: Japanese journal of radiology
Published Date: Jun 18, 2025

Abstract

PURPOSE: This study aimed to compare three publicly available deep learning models (TotalSegmentator, TotalVibeSegmentator, and PanSegNet) for automated pancreatic segmentation on magnetic resonance images and to evaluate their performance against human annotations in terms of segmentation accuracy, volumetric measurement, and intrapancreatic fat fraction (IPFF) assessment.

Authors

  • Yuki Sonoda
    Department of Radiology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan.
  • Shota Fujisawa
    Department of Radiology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan.
  • Mariko Kurokawa
    Department of Radiology, The University of Tokyo Hospital, Tokyo, Japan.
  • Wataru Gonoi
    Department of Radiology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
  • Shouhei Hanaoka
    Department of Radiology, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo, Japan.
  • Takeharu Yoshikawa
    The University of Tokyo Hospital.
  • Osamu Abe
    From the Department of Radiology, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo, Japan 113-8655.

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