System events: readily accessible features for surgical phase detection
System events: readily accessible features for surgical phase detection
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DOI：
10.1007/s11548-016-1409-0
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发表时间：
2016-06-01
影响因子：
3
通讯作者：
Hager, Gregory D.
中科院分区：
文献类型：
--
作者：
Malpani, Anand;Lea, Colin;Hager, Gregory D.
由于患者解剖结构和外科医生特定操作方式的高度变化，使用传感器数据的手术阶段识别具有挑战性。将外科手术分割成组成阶段对于住院医师培训、教育、自我审查和情境感知手术室技术具有重要实用性。相位标注是一项高度劳动密集型的任务，将大大受益于自动化solutions.We提出了一种新的方法，使用系统事件，例如，激活烧灼工具，很容易捕获在大多数外科手术。我们的方法涉及提取基于事件的功能超过90秒的时间间隔，并分配一个阶段标签到每个时间间隔。我们探讨了三种分类技术：支持向量机，随机森林和时间卷积神经网络。这些模型中的每一个都独立地预测每个时间间隔的标签。我们还研究了分段推理使用的半马尔可夫条件随机场的基础上，共同执行阶段分割和分类的方法。我们的方法在24个机器人辅助子宫切除术的数据集上进行了评估，我们的框架能够使用基于事件的特征在一组五个不同阶段（结扎、剥离、阴道切开术、袖状缝合和背景）上检测手术阶段，准确率为74%。袖带闭合（精确度：83%，召回率：98%）和夹层（精确度：75%，召回率：88%）类别的精确度和召回率值高于其他类别。预测相位序列与真实相位序列之间的归一化Levenshtein距离为25%。我们的研究结果表明，系统事件特征对于自动检测手术相位是有用的。事件包含无法从运动数据中获得的相位信息，并且需要高级计算机视觉算法才能从视频中提取。这些事件中的许多事件并不特定于机器人手术，并且可以很容易地在非机器人手术模式中记录。在未来的工作中，我们计划结合联合收割机的信息，从系统事件，工具的运动，和视频自动相位检测在外科手术。
Surgical phase recognition using sensor data is challenging due to high variation in patient anatomy and surgeon-specific operating styles. Segmenting surgical procedures into constituent phases is of significant utility for resident training, education, self-review, and context-aware operating room technologies. Phase annotation is a highly labor-intensive task and would benefit greatly from automated solutions.We propose a novel approach using system events-for example, activation of cautery tools-that are easily captured in most surgical procedures. Our method involves extracting event-based features over 90-s intervals and assigning a phase label to each interval. We explore three classification techniques: support vector machines, random forests, and temporal convolution neural networks. Each of these models independently predicts a label for each time interval. We also examine segmental inference using an approach based on the semi-Markov conditional random field, which jointly performs phase segmentation and classification. Our method is evaluated on a data set of 24 robot-assisted hysterectomy procedures.Our framework is able to detect surgical phases with an accuracy of 74 % using event-based features over a set of five different phases-ligation, dissection, colpotomy, cuff closure, and background. Precision and recall values for the cuff closure (Precision: 83 %, Recall: 98 %) and dissection (Precision: 75 %, Recall: 88 %) classes were higher than other classes. The normalized Levenshtein distance between predicted and ground truth phase sequence was 25 %.Our findings demonstrate that system events features are useful for automatically detecting surgical phase. Events contain phase information that cannot be obtained from motion data and that would require advanced computer vision algorithms to extract from a video. Many of these events are not specific to robotic surgery and can easily be recorded in non-robotic surgical modalities. In future work, we plan to combine information from system events, tool motion, and videos to automate phase detection in surgical procedures.