  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.
