Forestier Germain, Riffaud Laurent, Jannin Pierre
MIPS, University of Haute-Alsace, Mulhouse, France,
Int J Comput Assist Radiol Surg. 2015 Jun;10(6):833-41. doi: 10.1007/s11548-015-1195-0. Epub 2015 Apr 23.
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.
近年来，对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获取的数据中识别手术活动和手术阶段。这些情境感知系统有多种应用，包括：在手术过程中支持手术团队、改进自动监测、设计新的教学模式。
在本文中，我们使用外科医生执行活动的低级记录来自动预测当前（高级）手术阶段。我们增强了决策树算法，使其能够考虑手术活动的局部情境，并结合了层次聚类算法。
对22例腰椎间盘突出症手术进行了实验。在检测51489个单一活动的阶段时，我们获得了0.843的总体精度。我们还评估了该方法在噪声方面的鲁棒性。
我们表明，与仅考虑单一活动的方法相比，使用局部情境可以改善结果。实验表明，使用局部情境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改善预测。
