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<p>Gastric cancer (GC) is the fifth most common malignant tumor worldwide. Surgical resection remains the primary treatment for GC, with laparoscopic surgery recommended by several international guidelines. Due to complex perigastric vessels, standard D2 lymph node dissection (LND) in laparoscopic GC (LapGC) surgery is challenging. Careful dissection is required to expose, dissect, and ligate vessels without injury, ensuring radical LND. Computer vision has the potential to assist in the identification of key vessels during LapGC surgery, thereby reducing the risk of vascular injury. However, existing publicly available surgical anatomy datasets mainly focus on organ segmentation and simple surgeries. To address the clinical challenges and research needs outlined above, we present the LapGC Key Vascular Anatomy Dataset (LapGC-KVAD-30). This dataset was extracted from thirty complete surgical videos and contains annotations for fifteen types of key vessels across eight D2 LND scenes. The LapGC-KVAD-30 uniquely contains 5303 frames that showcase the dynamic process of key vessels from initial appearance to full exposure (or ligation), providing essential information for effective and safe LND.</p>

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Dynamic key vascular anatomy dataset for D2 lymph node dissection during laparoscopic gastric cancer surgery

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年5月29日

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  • Longfei Gou,
  • Haolin Wu,
  • Chang Chen,
  • Jiayu Lai,
  • Hua Yang,
  • Yuqing Qiu,
  • Boer Su,
  • Hongyu Wang,
  • Bingyu Zhao,
  • Xin Ye,
  • Jinming Li,
  • Xiaobing Bao,
  • Guoxin Li,
  • Jiang Yu,
  • Yanfeng Hu,
  • Qi Dou,
  • Hao Chen

摘要

Gastric cancer (GC) is the fifth most common malignant tumor worldwide. Surgical resection remains the primary treatment for GC, with laparoscopic surgery recommended by several international guidelines. Due to complex perigastric vessels, standard D2 lymph node dissection (LND) in laparoscopic GC (LapGC) surgery is challenging. Careful dissection is required to expose, dissect, and ligate vessels without injury, ensuring radical LND. Computer vision has the potential to assist in the identification of key vessels during LapGC surgery, thereby reducing the risk of vascular injury. However, existing publicly available surgical anatomy datasets mainly focus on organ segmentation and simple surgeries. To address the clinical challenges and research needs outlined above, we present the LapGC Key Vascular Anatomy Dataset (LapGC-KVAD-30). This dataset was extracted from thirty complete surgical videos and contains annotations for fifteen types of key vessels across eight D2 LND scenes. The LapGC-KVAD-30 uniquely contains 5303 frames that showcase the dynamic process of key vessels from initial appearance to full exposure (or ligation), providing essential information for effective and safe LND.