Abstract
Automated surgical workflow analysis and understanding can assist surgeons to standardize procedures and enhance post-surgical assessment and indexing, as well as, interventional monitoring. Computer-assisted interventional (CAI) systems based on video can perform workflow estimation through surgical instruments' recognition while linking them to an ontology of procedural phases. In this work, we adopt a deep learning paradigm to detect surgical instruments in cataract surgery videos which in turn feed a surgical phase inference recurrent network that encodes temporal aspects of phase steps within the phase classification. Our models present comparable to state-of-the-art results for surgical tool detection and phase recognition with accuracies of 99 and 78% respectively.
Abstract (translated)
自动化外科手术工作流程分析和理解可以帮助外科医生标准化程序，加强术后评估和索引，以及介入监测。基于视频的计算机辅助介入（CAI）系统可以通过手术器械的识别来执行工作流程估计，同时将它们与程序阶段的本体联系起来。在这项工作中，我们采用深度学习范例来检测白内障手术视频中的手术器械，这反过来又提供手术阶段推理复发网络，该网络编码阶段分类中阶段步骤的时间方面。我们的模型与手术工具检测和相位识别的最新结果相当，精度分别为99％和78％。
URL
https://arxiv.org/abs/1807.10565
