This technical report provides a detailed overview of Endoscapes, a dataset of laparoscopic cholecystectomy (LC) videos with highly intricate annotations targeted at automated assessment of the Critical View of Safety (CVS). Endoscapes comprises 201 LC videos with frames annotated sparsely but regularly with segmentation masks, bounding boxes, and CVS assessment by three different clinical experts. Altogether, there are 11090 frames annotated with CVS and 1933 frames annotated with tool and anatomy bounding boxes from the 201 videos, as well as an additional 422 frames from 50 of the 201 videos annotated with tool and anatomy segmentation masks. In this report, we provide detailed dataset statistics (size, class distribution, dataset splits, etc.) and a comprehensive performance benchmark for instance segmentation, object detection, and CVS prediction. The dataset and model checkpoints are publically available at https://github.com/CAMMA-public/Endoscapes.

翻译：本技术报告详细介绍了Endoscapes数据集，该数据集包含腹腔镜胆囊切除术（LC）手术视频，并针对安全关键视野（CVS）的自动化评估提供高度精细的标注。Endoscapes数据集包含201个LC手术视频，其帧图像以稀疏但规则的方式标注了分割掩膜、边界框以及由三位临床专家独立评估的CVS结果。总计包含来自201个视频的11090帧CVS标注数据和1933帧器械与解剖结构边界框标注数据，此外还从其中50个视频中额外提取422帧补充了器械与解剖结构分割掩膜标注。本报告提供了详细的数据集统计信息（规模、类别分布、数据集划分等），并建立了实例分割、目标检测和CVS预测的综合性能基准。该数据集及模型检查点已在https://github.com/CAMMA-public/Endoscapes 公开获取。