Gauze Detection and Segmentation in Minimally Invasive Surgery Video Using Convolutional Neural Networks.

ABSTRACT: Medical instruments detection in laparoscopic video has been carried out to increase the autonomy of surgical robots, evaluate skills or index recordings. However, it has not been extended to surgical gauzes. Gauzes can provide valuable information to numerous tasks in the operating room, but the lack of an annotated dataset has hampered its research. In this article, we present a segmentation dataset with 4003 hand-labelled frames from laparoscopic video. To prove the dataset potential, we analyzed several baselines: detection using YOLOv3, coarse segmentation, and segmentation with a U-Net. Our results show that YOLOv3 can be executed in real time but provides a modest recall. Coarse segmentation presents satisfactory results but lacks inference speed. Finally, the U-Net baseline achieve

SUBMITTER: Sanchez-Brizuela G 

PROVIDER: S-EPMC9319965 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature