Automatically recognizing surgical gestures is a crucial step towards a
thorough understanding of surgical skill. Possible areas of application include
automatic skill assessment, intra-operative monitoring of critical surgical
steps, and semi-automation of surgical tasks. Solutions that rely only on the
laparoscopic video and do not require additional sensor hardware are especially
attractive as they can be implemented at low cost in many scenarios. However,
surgical gesture recognition based only on video is a challenging problem that
requires effective means to extract both visual and temporal information from
the video. Previous approaches mainly rely on frame-wise feature extractors,
either handcrafted or learned, which fail to capture the dynamics in surgical
video. To address this issue, we propose to use a 3D Convolutional Neural
Network (CNN) to learn spatiotemporal features from consecutive video frames.
We evaluate our approach on recordings of robot-assisted suturing on a
bench-top model, which are taken from the publicly available JIGSAWS dataset.
Our approach achieves high frame-wise surgical gesture recognition accuracies
of more than 84%, outperforming comparable models that either extract only
spatial features or model spatial and low-level temporal information
separately. For the first time, these results demonstrate the benefit of
spatiotemporal CNNs for video-based surgical gesture recognition.

采用三维卷积神经网络从连续视频帧中学习时空特征，实现视觉手术姿势识别，可用于手术技能评估、手术操作中的监控和手术任务的半自动化等多种应用。在针对 JIGSAWS 数据库中的机器人辅助缝合视频的测试中，通过对空间时间特征建模，该方法实现了高于 84% 的手术姿态识别准确性，是视频手术姿态识别的重要进展。