Cholec80-CVS 安全关键视图数据集
开放腹腔镜胆囊切除术视频数据集,含专家标注的安全关键视图(CVS)标准,用于AI辅助手术安全评估。

基本信息
资源简介
Cholec80-CVS 安全关键视图数据集
【数据集背景】
本数据集源站标题为《Cholec80-CVS: An open dataset with an evaluation of Strasberg''s critical view of safety for Artificial Intelligence》。源站首次发布:2023-04-05。
【数据内容】
源站原始描述:Strasberg''s criteria to detect a critical view of safety is a widely known strategy to reduce bile duct injuries during laparoscopic cholecystectomy. In spite of its popularity and efficiency, recent studies have shown that human miss-identification errors have led to important bile duct injuries occurrence rates. Developing tools based on artificial intelligence that facilitate the identification of a critical view of safety in cholecystectomy surgeries can potentially minimize the risk of such…
【数据结构与技术规格】
文件数 1 个,合计 24.82 KB。cholec80-CVS.xlsx:application/vnd.openxmlformats-officedocument.spreadsheetml.sheet,24.82 KB。数据集 DOI:10.6084/m9.figshare.22183885.v1。
【主题与分类】
源站学科分类:Surgery、Artificial Intelligence and Image Processing。主题标签:Laparoscopic cholecysectomy、surgery prediction、Strasberg''s criteria、Video annotations、Cholec80、Image processing。
【适用方向】
影像类数据可用于医学图像分析模型(检测、分割、分类)的训练与评测,也可用于标注质量与成像参数相关性的研究。
【获取与许可】
源站页面:https://springernature.figshare.com/articles/dataset/Cholec80-CVS_An_open_dataset_with_an_evaluation_of_Strasberg_s_critical_view_of_safety_for_Artificial_Intelligence/22183885
使用许可:CC0(https://creativecommons.org/publicdomain/zero/1.0/)。
规范引用:Ríos, Manuel Sebastián; Guillén, Camilo Andrés; Giraldo, Luis Felipe; Molina, María Alejandra; Londono, Daniella; Zapata, Sebastián; et al. (2023). Cholec80-CVS: An open dataset with an evaluation of Strasberg''s critical view of safety for Artificial Intelligence. figshare. Dataset
下载信息
注册下载
需要注册 Kaggle 账号并登录后下载,适合需要跟踪下载记录和使用 API 的用户。
暂未开放公开下载
无需注册即可直接获取公开样本或文档,适合快速预览和评估数据集质量。
免登录有偿下载
公开数据集受托下载与技术交付服务。
提供高速下载与技术交付服务(收技术服务费,非数据销售)
暂未开放当前数据集主要来源为 Kaggle 公开托管,完整影像包建议通过原始链接或 Kaggle API 获取。
使用方式
数据集获取(Figshare)
- 源站标题:Cholec80-CVS: An open dataset with an evaluation of Strasberg’'s critical view of safety for Artificial Intelligence
- DOI:10.6084/m9.figshare.22183885.v1
- 发布日期:2023-04-05
- 文件数:1 个
- 文件总体积:24.82 KB
- 源站页面:https://springernature.figshare.com/articles/dataset/Cholec80-CVS_An_open_dataset_with_an_evaluation_of_Strasberg_s_critical_view_of_safety_for_Artificial_Intelligence/22183885
命令行下载
curl -L -o "cholec80-CVS.xlsx" "https://ndownloader.figshare.com/files/39419062"
文件清单
cholec80-CVS.xlsx(24.82 KB)
文件总体积 24.82 KB(源站 API 实测)。
数据加载示例(图像类)
from PIL import Image
import glob, os
files = (glob.glob(os.path.join(path, "**", "*.png"), recursive=True)
+ glob.glob(os.path.join(path, "**", "*.jpg"), recursive=True)
+ glob.glob(os.path.join(path, "**", "*.tif"), recursive=True))
print("图像文件数:", len(files))
img = Image.open(files[0]); print("尺寸/模式:", img.size, img.mode)
# torchvision Dataset 方式:
# from torchvision import datasets
# ds = datasets.ImageFolder(path) # 要求 子目录=类别
目录组织与标注格式以源站说明和下载后实际文件为准。
数据说明
Strasberg’‘s criteria to detect a critical view of safety is a widely known strategy to reduce bile duct injuries during laparoscopic cholecystectomy. In spite of its popularity and efficiency, recent studies have shown that human miss-identification errors have led to important bile duct injuries occurrence rates. Developing tools based on artificial intelligence that facilitate the identification of a critical view of safety in cholecystectomy surgeries can potentially minimize the risk of such injuries. With this goal in mind, we present Cholec80-CVS, the first open dataset with video annotations of Strasberg’'s Critical View of Safety (CVS) criteria. Our dataset contains CVS criteria annotations provided by skilled surgeons for all videos in the well-known Cholec80 open video dataset. We consider that Cholec80-CVS is the first step towards the creation of intelligent systems that can assist humans during laparoscopic cholecystectomy.
许可
CC0
引用
学术使用请引用 DOI 10.6084/m9.figshare.22183885.v1(source: https://springernature.figshare.com/articles/dataset/Cholec80-CVS_An_open_dataset_with_an_evaluation_of_Strasberg_s_critical_view_of_safety_for_Artificial_Intelligence/22183885)。
数据缺失?
依托陕西、四川两大基地,我们与超过十家三甲医院建立直接合作关系,覆盖合规授权、采集治理、专业标注、数据交付的全流程,为AI医疗团队提供即拿即用的高质量临床数据。
⚡️ 需要数据支持或标注服务?立即联系我们获取专业方案。




