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

IF 5.8 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES
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
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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.

腹腔镜胃癌手术中D2淋巴结清扫的动态关键血管解剖数据集。
胃癌是世界上第五大最常见的恶性肿瘤。手术切除仍然是胃癌的主要治疗方法,一些国际指南推荐腹腔镜手术。由于胃周血管复杂,腹腔镜胃癌(LapGC)手术中标准D2淋巴结清扫(LND)具有挑战性。需要仔细的解剖,在不损伤的情况下暴露、解剖和结扎血管,确保根治性LND。计算机视觉有可能在LapGC手术中帮助识别关键血管,从而降低血管损伤的风险。然而,现有的公开的外科解剖数据集主要集中在器官分割和简单的手术。为了应对上述临床挑战和研究需求,我们提出了LapGC关键血管解剖数据集(LapGC- kvad -30)。该数据集从30个完整的手术视频中提取,包含8个D2 LND场景中15种关键血管的注释。LapGC-KVAD-30独特地包含5303帧,展示了关键血管从最初的外观到完全暴露(或结扎)的动态过程,为有效和安全的LND提供了必要的信息。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Scientific Data
Scientific Data Social Sciences-Education
CiteScore
11.20
自引率
4.10%
发文量
689
审稿时长
16 weeks
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.
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