2026-04-27T21:44:17Zhttps://uvadoc.uva.es/oai/request

oai:uvadoc.uva.es:10324/657612025-02-18T11:14:13Zcom_10324_966com_10324_952com_10324_894col_10324_967

00925njm 22002777a 4500 dc Sánchez Brizuela, Guillermo author Santos Criado, Francisco Javier author Sanz Gobernado, Daniel author Fuente López, Eusebio de la author Fraile Marinero, Juan Carlos author Pérez Turiel, Javier author Cisnal De La Rica, Ana author 2022 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 achieves a good speed-quality compromise running above 30 FPS while obtaining an IoU of 0.85. The accuracy reached by U-Net and its execution speed demonstrate that precise and real-time gauze segmentation can be achieved, training convolutional neural networks on the proposed dataset. Gauze Detection and Segmentation in Minimally Invasive Surgery Video Using Convolutional Neural Networks. Sensors 2022, 22, 5180 https://uvadoc.uva.es/handle/10324/65761 10.3390/s22145180 5180 14 Sensors 22 1424-8220 Gauze detection and segmentation in minimally invasive surgery video using convolutional Neural Networks