ArticleScientific data2025
Dynamic key vascular anatomy dataset for D2 lymph node dissection during laparoscopic gastric cancer surgery.
Article in Scientific data, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- ESD-VesNet: uncertainty-aware vessel segmentation network for endoscopic submucosal dissection with hard negative mining.International journal of computer assisted radiology and surgery · 2026Article
- Dynamic key vascular anatomy dataset for D2 lymph node dissection during laparoscopic gastric cancer surgery.Scientific data · 2025Article
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Authors and funding
17 authors.
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Abstract
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.
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