Using spatio-temporal hybrid features for detecting bleeding point in laparoscopic surgery

Jigang Jiang; Yao Lu; Surong Hua

doi:10.1117/12.2589338

27 January 2021 Using spatio-temporal hybrid features for detecting bleeding point in laparoscopic surgery

Jigang Jiang, Yao Lu, Surong Hua

Proceedings Volume 11720, Twelfth International Conference on Graphics and Image Processing (ICGIP 2020); 117200C (2021) https://doi.org/10.1117/12.2589338
Event: Twelfth International Conference on Graphics and Image Processing, 2020, Xi'an, China

Abstract

Research on video bleeding has become a hot topic in the computer-assisted surgery. We do further research to directly detect the bleeding point. Locating bleeding point can help surgeons stop bleeding quickly. We propose a mixed RCNN model based on faster RCNN for bleeding point detection in the laparoscopic surgery videos. And we make three contributions: (1) we propose an idea for hemostasis support system which can more directly assist the surgeons. (2) we show the blood’s optical flow to improve bleeding point detection. (3) both arterial and venous bleeding can be detected. Experimental results on our laparoscopic surgery video datasets show that our approach performs very well in the bleeding point location and recognition.

Citation Download Citation

Jigang Jiang, Yao Lu, and Surong Hua "Using spatio-temporal hybrid features for detecting bleeding point in laparoscopic surgery", Proc. SPIE 11720, Twelfth International Conference on Graphics and Image Processing (ICGIP 2020), 117200C (27 January 2021); https://doi.org/10.1117/12.2589338

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