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Related Experiment Video

Updated: Jan 14, 2026

Whole-mount Immunohistochemical Analysis for Embryonic Limb Skin Vasculature: a Model System to Study Vascular Branching Morphogenesis in Embryo
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Whole-mount Immunohistochemical Analysis for Embryonic Limb Skin Vasculature: a Model System to Study Vascular Branching Morphogenesis in Embryo

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Deep learning-based vessel and nerve recognition model for lateral lymph node dissection: a retrospective feasibility

Shoma Sasaki1,2, Daichi Kitaguchi1,2, Tomohiro Noda1

  • 1Department of Colorectal Surgery, National Cancer Center Hospital East, 6-5-1, Kashiwanoha, Kashiwa-City, 277-8577, Chiba, Japan.

Langenbeck'S Archives of Surgery
|October 27, 2025
PubMed
Summary

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A new deep learning model can automatically identify critical nerves and blood vessels during rectal cancer surgery. This AI tool aids surgeons in laparoscopic lateral lymph node dissection, enhancing safety and efficiency.

Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Surgery
  • Surgical Oncology

Background:

  • Lateral lymph node dissection for rectal cancer is complex due to vital nearby structures.
  • Preserving the external iliac artery, external iliac vein, and obturator nerve is crucial for postoperative function.
  • Accurate identification of these structures during surgery is paramount.

Purpose of the Study:

  • To develop a deep learning-based semantic segmentation model for automatic recognition of critical anatomical structures.
  • To visualize the external iliac artery, external iliac vein, and obturator nerve during laparoscopic lateral lymph node dissection.
  • To enhance the safety and efficiency of rectal cancer surgery.

Main Methods:

  • Utilized intraoperative video data from 22 laparoscopic lateral lymph node dissections.
Keywords:
Artificial intelligenceDeep learningLaparoscopic lateral lymph node dissectionSemantic segmentationVessel and nerve recognition

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  • Extracted and annotated 992 still images, delineating the external iliac artery, vein, and obturator nerve.
  • Trained a semantic segmentation model with pixel-level labels and evaluated performance using Dice coefficient and cross-validation.

    • Main Results:

    • The model achieved Dice coefficients of 0.789 for the obturator nerve, 0.736 for the external iliac artery, and 0.574 for the external iliac vein.
    • Demonstrated near real-time processing with an inference speed of 12.7 fps.
    • The model successfully recognized key anatomical structures with reasonable accuracy.

    Conclusions:

    • A deep learning semantic segmentation model can automatically identify critical structures during laparoscopic lateral lymph node dissection.
    • The model provides a foundation for developing advanced surgical navigation systems.
    • This technology has the potential to significantly improve the safety and efficiency of rectal cancer treatment.