Automatic data-driven real-time segmentation and recognition of surgical workflow
Automatic data-driven real-time segmentation and recognition of surgical workflow
复制标题
DOI：
10.1007/s11548-016-1371-x
复制
发表时间：
2016-06-01
影响因子：
3
通讯作者：
Jannin, Pierre
中科院分区：
文献类型：
--
作者：
Dergachyova, Olga;Bouget, David;Jannin, Pierre
为了扩大外科手术人员在手术室内的感知和行动，医学界对情景感知系统表示了越来越大的兴趣。由于需要准确识别手术流程，这类系统利用了来自各种可用传感器的数据。在本文中，我们提出了一种完全数据驱动的实时分割和识别方法，利用视频数据和器械使用信号的组合，不利用任何先验知识。我们还引入了新的验证度量来评估工作流检测，分割和识别基于四个阶段的过程。首先，在学习过程中，从数据标注中自动构建手术过程模型，以指导后续过程。其次，结合低级视觉线索和仪器信息来描述数据样本。然后，在第三阶段，使用这些描述来训练一组能够将一个手术阶段与其他手术阶段区分开来的AdaBoost分类器。最后，将AdaBoost响应作为隐半马尔可夫模型的输入，在MICCAI EndoVis Challenges数据集上实现了7个阶段的准确率和91%的召回率。与仅基于一种数据类型的分析相比，视觉特征和仪器信号的组合可以更好地分割、减少检测延迟和发现正确的相位顺序。
With the intention of extending the perception and action of surgical staff inside the operating room, the medical community has expressed a growing interest towards context-aware systems. Requiring an accurate identification of the surgical workflow, such systems make use of data from a diverse set of available sensors. In this paper, we propose a fully data-driven and real-time method for segmentation and recognition of surgical phases using a combination of video data and instrument usage signals, exploiting no prior knowledge. We also introduce new validation metrics for assessment of workflow detection.The segmentation and recognition are based on a four-stage process. Firstly, during the learning time, a Surgical Process Model is automatically constructed from data annotations to guide the following process. Secondly, data samples are described using a combination of low-level visual cues and instrument information. Then, in the third stage, these descriptions are employed to train a set of AdaBoost classifiers capable of distinguishing one surgical phase from others. Finally, AdaBoost responses are used as input to a Hidden semi-Markov Model in order to obtain a final decision.On the MICCAI EndoVis challenge laparoscopic dataset we achieved a precision and a recall of 91 % in classification of 7 phases.Compared to the analysis based on one data type only, a combination of visual features and instrument signals allows better segmentation, reduction of the detection delay and discovery of the correct phase order.