Purpose: Advances in deep learning have resulted in effective models for
surgical video analysis; however, these models often fail to generalize across
medical centers due to domain shift caused by variations in surgical workflow,
camera setups, and patient demographics. Recently, object-centric learning has
emerged as a promising approach for improved surgical scene understanding,
capturing and disentangling visual and semantic properties of surgical tools
and anatomy to improve downstream task performance. In this work, we conduct a
multi-centric performance benchmark of object-centric approaches, focusing on
Critical View of Safety assessment in laparoscopic cholecystectomy, then
propose an improved approach for unseen domain generalization.
Methods: We evaluate four object-centric approaches for domain
generalization, establishing baseline performance. Next, leveraging the
disentangled nature of object-centric representations, we dissect one of these
methods through a series of ablations (e.g. ignoring either visual or semantic
features for downstream classification). Finally, based on the results of these
ablations, we develop an optimized method specifically tailored for domain
generalization, LG-DG, that includes a novel disentanglement loss function.
Results: Our optimized approach, LG-DG, achieves an improvement of 9.28% over
the best baseline approach. More broadly, we show that object-centric
approaches are highly effective for domain generalization thanks to their
modular approach to representation learning.
Conclusion: We investigate the use of object-centric methods for unseen
domain generalization, identify method-agnostic factors critical for
performance, and present an optimized approach that substantially outperforms
existing methods.

通过多中心性能基准测试，我们研究了面向未见域泛化的以目标为中心的方法，确定了关键的性能因素，并提出了一种优化方法，明显优于现有方法。

手术场景的潜在图表示优化及零样本领域转移

Optimizing Latent Graph Representations of Surgical Scenes for Zero-Shot  Domain Transfer

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

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。