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Accurate preoperative mapping of abdominal vasculature is essential in colorectal surgery to reduce intraoperative bleeding and postoperative ischemia. We developed deep learning models for automated 3D segmentation of arteries and veins from dual-phase contrast-enhanced CT scans. Our dataset included 55 patients with arterial and venous phase scans, where major vessels down to third-order branching were manually annotated. Three nnU-Net variants (standard 3D U-Net, residual encoder U-Net, and SkeletonRecall) were trained independently for arteries and veins using five-fold cross-validation. Segmentation performance was evaluated using Dice score, centerline Dice score, sensitivity, and precision. All models achieved comparable Dice and centerline Dice scores, with slightly better results for veins segmentation. SkeletonRecall showed the highest sensitivity and superior vessel continuity, despite lower precision. On the independent test set, Dice scores were lower due to incomplete ground truth, yet SkeletonRecall correctly captured vessels up to third-order branching. The trained models are publicly available at https://github.com/LERCO-FNO/Abdominal-Vessels-Segmentation.

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Automatic Deep Learning-Based Segmentation of Abdominal Vessels in CT Scans

  • 会议论文

  • 发表日期：

    2026年

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  • Michal Nohel,
  • Katerina Krejci,
  • Constantin Ulrich,
  • Maximilian Rokuss,
  • Yannick Kirchhoff,
  • Jiri Chmelik,
  • Stefan Reguli,
  • Jan Hrubovcak,
  • Lubomir Martinek,
  • Lukas Knybel

摘要

Accurate preoperative mapping of abdominal vasculature is essential in colorectal surgery to reduce intraoperative bleeding and postoperative ischemia. We developed deep learning models for automated 3D segmentation of arteries and veins from dual-phase contrast-enhanced CT scans. Our dataset included 55 patients with arterial and venous phase scans, where major vessels down to third-order branching were manually annotated. Three nnU-Net variants (standard 3D U-Net, residual encoder U-Net, and SkeletonRecall) were trained independently for arteries and veins using five-fold cross-validation. Segmentation performance was evaluated using Dice score, centerline Dice score, sensitivity, and precision. All models achieved comparable Dice and centerline Dice scores, with slightly better results for veins segmentation. SkeletonRecall showed the highest sensitivity and superior vessel continuity, despite lower precision. On the independent test set, Dice scores were lower due to incomplete ground truth, yet SkeletonRecall correctly captured vessels up to third-order branching. The trained models are publicly available at https://github.com/LERCO-FNO/Abdominal-Vessels-Segmentation.