Automatic phase prediction from low-level surgical activities
Automatic phase prediction from low-level surgical activities
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DOI：
10.1007/s11548-015-1195-0
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发表时间：
2015-06-01
影响因子：
3
通讯作者：
Jannin, Pierre
中科院分区：
文献类型：
--
作者：
Forestier, Germain;Riffaud, Laurent;Jannin, Pierre
近年来，外科手术活动的分析越来越受到关注。已经提出了几种方法来从手术室中获取的数据中识别手术活动和手术阶段。这些上下文感知系统有多种应用，包括：支持手术团队在干预过程中，提高自动监控，设计新的教学paradigms.In本文中，我们使用低级别的记录的活动，由外科医生进行自动预测的当前（高级别）阶段的手术。我们增加了决策树算法的能力，考虑当地的手术活动和层次聚类算法。实验进行了22例腰椎间盘突出症的手术。我们在检测51，489个单个活动的相位时获得了0.843的总体精度。我们还评估了该方法对噪声的鲁棒性。我们表明，与只考虑单个活动的方法相比，使用局部上下文可以改善结果。实验表明，局部上下文的使用使我们的方法对噪声非常鲁棒，并且首先对输入数据进行聚类可以提高预测。
Analyzing surgical activities has received a growing interest in recent years. Several methods have been proposed to identify surgical activities and surgical phases from data acquired in operating rooms. These context-aware systems have multiple applications including: supporting the surgical team during the intervention, improving the automatic monitoring, designing new teaching paradigms.In this paper, we use low-level recordings of the activities that are performed by a surgeon to automatically predict the current (high-level) phase of the surgery. We augment a decision tree algorithm with the ability to consider the local context of the surgical activities and a hierarchical clustering algorithm.Experiments were performed on 22 surgeries of lumbar disk herniation. We obtained an overall precision of 0.843 in detecting phases of 51,489 single activities. We also assess the robustness of the method with regard to noise.We show that using the local context allows us to improve the results compared with methods only considering single activity. Experiments show that the use of the local context makes our method very robust to noise and that clustering the input data first improves the predictions.