An entropy‐based approach to detect and localize intraoperative bleeding during minimally invasive surgery

Background During minimally invasive surgery (either robotic or traditional laparoscopic), vascular injuries may occur because of inadvertent surgical tool movements or actions. These vascular injuries can lead to arterial or venous bleeding with varying degrees of severity that may be life‐threatening. Materials and Methods Given that a bloody spot is characterized by homogenous and uniform texture, our algorithm automatically scans the entire surgical video frame‐ by‐ frame using a local entropy filter to segment each image into different regions sequence. By comparing changes in entropy in the frames sequences, the algorithm detects the moment of bleeding occurrence and its pixel location. We preliminarily tested the algorithm using ten minimally invasive‐surgery videos., each of which contains one surgical‐tool‐induced bleeding. Results Our results show that the algorithm can detect bleeding within 0.635 s, on average, after their occurrences and locate the bleeding sources within, on average, 2.5% of discrepancy in pixels from their origins. Conclusion In this study, we present a novel and promising local‐entropy‐based image processing algorithm that detects spurts of blood and locate their source in real‐time.