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Article

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

1
Instituto de las Tecnologías Avanzadas de la Producción (ITAP), Universidad de Valladolid, Paseo del Cauce 59, 47011 Valladolid, Spain
2
Escuela Técnica Superior de Ingenieros Industriales, Universidad Politécnica de Madrid, Calle de José Gutiérrez Abascal, 2, 28006 Madrid, Spain
*
Author to whom correspondence should be addressed.
Academic Editors: Tamás Haidegger and Axel Krieger
Sensors 2022, 22(14), 5180; https://doi.org/10.3390/s22145180
Received: 13 June 2022 / Revised: 30 June 2022 / Accepted: 7 July 2022 / Published: 11 July 2022
(This article belongs to the Special Issue Medical Robotics)
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. View Full-Text
Keywords: convolutional neural networks; image segmentation; image object detection; surgical tool detection; minimally invasive surgery convolutional neural networks; image segmentation; image object detection; surgical tool detection; minimally invasive surgery
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MDPI and ACS Style

Sánchez-Brizuela, G.; Santos-Criado, F.-J.; Sanz-Gobernado, D.; de la Fuente-López, E.; Fraile, J.-C.; Pérez-Turiel, J.; Cisnal, A. Gauze Detection and Segmentation in Minimally Invasive Surgery Video Using Convolutional Neural Networks. Sensors 2022, 22, 5180. https://doi.org/10.3390/s22145180

AMA Style

Sánchez-Brizuela G, Santos-Criado F-J, Sanz-Gobernado D, de la Fuente-López E, Fraile J-C, Pérez-Turiel J, Cisnal A. Gauze Detection and Segmentation in Minimally Invasive Surgery Video Using Convolutional Neural Networks. Sensors. 2022; 22(14):5180. https://doi.org/10.3390/s22145180

Chicago/Turabian Style

Sánchez-Brizuela, Guillermo, Francisco-Javier Santos-Criado, Daniel Sanz-Gobernado, Eusebio de la Fuente-López, Juan-Carlos Fraile, Javier Pérez-Turiel, and Ana Cisnal. 2022. "Gauze Detection and Segmentation in Minimally Invasive Surgery Video Using Convolutional Neural Networks" Sensors 22, no. 14: 5180. https://doi.org/10.3390/s22145180

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