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 this https URL

这份技术报告提供了对 Endoscapes 数据集的详细概述，该数据集是一组镜内胆囊切除术（LC）视频，具有高度复杂的标注，旨在自动评估安全关键视图（CVS）；报道了数据集的详细统计信息和实例分割、目标检测和 CVS 预测的全面性能基准。

外科手术场景分割、目标检测和安全评估的内窥镜数据集：官方划分和基准

The Endoscapes Dataset for Surgical Scene Segmentation, Object  Detection, and Critical View of Safety Assessment: Official Splits and  Benchmark

Purpose: General consensus amongst researchers and industry points to a lack
of large, representative annotated datasets as the biggest obstacle to progress
in the field of surgical data science. Self-supervised learning represents a
solution to part of this problem, removing the reliance on annotations.
However, the robustness of current self-supervised learning methods to domain
shifts remains unclear, limiting our understanding of its utility for
leveraging diverse sources of surgical data. Methods: In this work, we employ
self-supervised learning to flexibly leverage diverse surgical datasets,
thereby learning taskagnostic representations that can be used for various
surgical downstream tasks. Based on this approach, to elucidate the impact of
pre-training on downstream task performance, we explore 22 different
pre-training dataset combinations by modulating three variables: source
hospital, type of surgical procedure, and pre-training scale (number of
videos). We then finetune the resulting model initializations on three diverse
downstream tasks: namely, phase recognition and critical view of safety in
laparoscopic cholecystectomy and phase recognition in laparoscopic
hysterectomy. Results: Controlled experimentation highlights sizable boosts in
performance across various tasks, datasets, and labeling budgets. However, this
performance is intricately linked to the composition of the pre-training
dataset, robustly proven through several study stages. Conclusion: The
composition of pre-training datasets can severely affect the effectiveness of
SSL methods for various downstream tasks and should critically inform future
data collection efforts to scale the application of SSL methodologies.
Keywords: Self-Supervised Learning, Transfer Learning, Surgical Computer
Vision, Endoscopic Videos, Critical View of Safety, Phase Recognition

通过自我监督学习，在不同手术数据集上进行预训练，从而灵活地利用多样化的手术数据，为各种手术下游任务学习与任务无关的表示，研究发现预训练数据集的组成严重影响自我监督学习方法在各种下游任务上的有效性，对于规模化应用自我监督学习方法应充分考虑预训练数据集的组成。

外科计算机视觉的启动

Jumpstarting Surgical Computer Vision

Cholecystectomy (gallbladder removal) is one of the most common procedures in
the US, with more than 1.2M procedures annually. Compared with classical open
cholecystectomy, laparoscopic cholecystectomy (LC) is associated with
significantly shorter recovery period, and hence is the preferred method.
However, LC is also associated with an increase in bile duct injuries (BDIs),
resulting in significant morbidity and mortality. The primary cause of BDIs
from LCs is misidentification of the cystic duct with the bile duct. Critical
view of safety (CVS) is the most effective of safety protocols, which is said
to be achieved during the surgery if certain criteria are met. However, due to
suboptimal understanding and implementation of CVS, the BDI rates have remained
stable over the last three decades. In this paper, we develop deep-learning
techniques to automate the assessment of CVS in LCs. An innovative aspect of
our research is on developing specialized learning techniques by incorporating
domain knowledge to compensate for the limited training data available in
practice. In particular, our CVS assessment process involves a fusion of two
segmentation maps followed by an estimation of a certain region of interest
based on anatomical structures close to the gallbladder, and then finally
determination of each of the three CVS criteria via rule-based assessment of
structural information. We achieved a gain of over 11.8% in mIoU on relevant
classes with our two-stream semantic segmentation approach when compared to a
single-model baseline, and 1.84% in mIoU with our proposed Sobel loss function
when compared to a Transformer-based baseline model. For CVS criteria, we
achieved up to 16% improvement and, for the overall CVS assessment, we achieved
5% improvement in balanced accuracy compared to DeepCVS under the same
experiment settings.

我们开发了深度学习技术来自动评估腹腔镜胆囊切除术中的安全视野，通过结构信息的规则评估确定三个安全视野标准中的每一个标准，并在实践中基于领域知识开发专门的学习技术，以弥补有限的训练数据。