Amplifying action-context greater: image segmentation-guided intraoperative active bleeding detection

Abstract

The intraoperative active bleeding (iAB) detection model can be used for image-guided surgery and as a significant statistical index in predicting patient outcomes after surgery. However, detecting iAB is difficult due to the similarity between active and non-active bleeding or active bleeding in a small area. Using the spatial and temporal characteristics of the iAB area within frames simultaneously can overcome this. We propose a novel training method that can adequately fuse image segmentation and temporal action localisation models for effective iAB detection. The proposed active bleeding detection model has the following supervision process: First, annotate temporal localisation information for active bleeding that is relatively easy to annotate. Next, in the active bleeding section, where temporal localisation is annotated, spatial localisation information for selected frames is annotated and used as auxiliary information for active bleeding detection. We constructed a cross-validation set of 40 robotic subtotal gastrectomies and verified the ability of an active bleeding model guided by image segmentation information to bring improvements to the active bleeding recognition task. In addition, we applied performance evaluation for outcome analysis by measuring errors in iAB duration and counting in surgical videos for each algorithm.1