{"title":"Dynamic key vascular anatomy dataset for D2 lymph node dissection during laparoscopic gastric cancer surgery.","authors":"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","doi":"10.1038/s41597-025-05255-7","DOIUrl":null,"url":null,"abstract":"<p><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>","PeriodicalId":21597,"journal":{"name":"Scientific Data","volume":"12 1","pages":"903"},"PeriodicalIF":5.8000,"publicationDate":"2025-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12123031/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Scientific Data","FirstCategoryId":"103","ListUrlMain":"https://doi.org/10.1038/s41597-025-05255-7","RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MULTIDISCIPLINARY SCIENCES","Score":null,"Total":0}
引用次数: 0
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.
期刊介绍:
Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data.
The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.