Evaluation of artificial-intelligence-based liver segmentation and its application for longitudinal liver volume measurement.

Journal: Abdominal radiology (New York)
Published Date: Jun 10, 2025

Abstract

BACKGROUND: Accurate liver-volume measurements from CT scans are essential for treatment planning, particularly in liver resection cases, to avoid postoperative liver failure. However, manual segmentation is time-consuming and prone to variability. Advancements in artificial intelligence (AI), specifically convolutional neural networks, have enhanced liver segmentation accuracy. We aimed to identify optimal CT phases for AI-based liver volume estimation and apply the model to track liver volume changes over time. We also evaluated temporal changes in liver volume in participants without liver disease.

Authors

  • Rina Kimura
    Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital, Sapporo, Hokkaido, Japan.
  • Kenji Hirata
    Department of Diagnostic Imaging, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, Sapporo, Japan.
  • Satonori Tsuneta
    Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital, N14 W5, Kita-Ku, Sapporo, Hokkaido, 060-8648, Japan.
  • Junki Takenaka
    Department of Diagnostic Imaging, Faculty of Medicine, Hokkaido University, Sapporo, Japan.
  • Shiro Watanabe
    Department of Diagnostic Imaging, Hokkaido University Graduate School of Medicine, N15 W7, Kita-ku, Sapporo, 0608638, Japan.
  • Daisuke Abo
    Department of Diagnostic Imaging, Faculty of Medicine, Hokkaido University, Sapporo, Japan.
  • Kohsuke Kudo
    Departments of Diagnostic Imaging, Hokkaido University Graduate School of Medicine, kita 15, nishi 7, kita-ku, Sapporo, Hokkaido, 060-8638, Japan.

Keywords

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