A spatio-temporal network for video semantic segmentation in surgical videos.

PURPOSE

Semantic segmentation in surgical videos has applications in intra-operative guidance, post-operative analytics and surgical education. Models need to provide accurate predictions since temporally inconsistent identification of anatomy can hinder patient safety. We propose a novel architecture for modelling temporal relationships in videos to address these issues.

METHODS

We developed a temporal segmentation model that includes a static encoder and a spatio-temporal decoder. The encoder processes individual frames whilst the decoder learns spatio-temporal relationships from frame sequences. The decoder can be used with any suitable encoder to improve temporal consistency.

RESULTS

Model performance was evaluated on the CholecSeg8k dataset and a private dataset of robotic Partial Nephrectomy procedures. Mean Intersection over Union improved by 1.30% and 4.27% respectively for each dataset when the temporal decoder was applied. Our model also displayed improvements in temporal consistency up to 7.23%.

CONCLUSIONS

This work demonstrates an advance in video segmentation of surgical scenes with potential applications in surgery with a view to improve patient outcomes. The proposed decoder can extend state-of-the-art static models, and it is shown that it can improve per-frame segmentation output and video temporal consistency.