仁济结直肠肿瘤病变内镜黏膜下剥离术视频数据集

数据集获取（Figshare）

• 源站标题：Renji endoscopic submucosal dissection video data set for colorectal neoplastic lesions
• DOI：10.6084/m9.figshare.28737686.v1
• 发布日期：2025-08-07
• 文件数：1 个
• 文件总体积：201.98 GB
• 源站页面：https://springernature.figshare.com/articles/dataset/Renji_endoscopic_submucosal_dissection_video_data_set_for_colorectal_neoplastic_lesions/28737686

命令行下载

curl -L -o "Renji ESD for CNLs.rar" "https://ndownloader.figshare.com/files/56338025"

文件清单

• Renji ESD for CNLs.rar（201.98 GB）

文件总体积 201.98 GB（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

Artificial intelligence advancements have significantly enhanced computer-aided intervention, learning among surgeons, and analysis of surgical videos post-operation, substantially elevating surgical expertise and patient outcomes. Recognition systems for endoscopic surgical phases using deep learning algorithms heavily rely on comprehensive annotated datasets. Our research presents the Renji dataset featuring videos of endoscopic submucosal dissection (ESD) for colorectal neoplastic lesions (CNLs), which includes 30 procedural recordings with 130,298 phase-specific annotations collaboratively labeled by a team of three specialists in endoscopy. To our knowledge, this represents the first openly accessible collection of ESD videos specifically targeting CNLs treatment, and we anticipate this work will help establish standards for constructing similar ESD databases.

许可

CC0

引用

学术使用请引用 DOI 10.6084/m9.figshare.28737686.v1（source: https://springernature.figshare.com/articles/dataset/Renji_endoscopic_submucosal_dissection_video_data_set_for_colorectal_neoplastic_lesions/28737686）。