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

Using 3D Convolutional Neural Networks to Learn Spatiotemporal Features for Automatic Surgical Gesture Recognition in Video