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Gauze Detection and Segmentation in Minimally Invasive Surgery Video Using Convolutional Neural Networks
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 la...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9319965/ https://www.ncbi.nlm.nih.gov/pubmed/35890857 http://dx.doi.org/10.3390/s22145180 |
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author | Sánchez-Brizuela, Guillermo Santos-Criado, Francisco-Javier Sanz-Gobernado, Daniel de la Fuente-López, Eusebio Fraile, Juan-Carlos Pérez-Turiel, Javier Cisnal, Ana |
author_facet | Sánchez-Brizuela, Guillermo Santos-Criado, Francisco-Javier Sanz-Gobernado, Daniel de la Fuente-López, Eusebio Fraile, Juan-Carlos Pérez-Turiel, Javier Cisnal, Ana |
author_sort | Sánchez-Brizuela, Guillermo |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-9319965 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-93199652022-07-27 Gauze Detection and Segmentation in Minimally Invasive Surgery Video Using Convolutional Neural Networks Sánchez-Brizuela, Guillermo Santos-Criado, Francisco-Javier Sanz-Gobernado, Daniel de la Fuente-López, Eusebio Fraile, Juan-Carlos Pérez-Turiel, Javier Cisnal, Ana Sensors (Basel) Article 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. MDPI 2022-07-11 /pmc/articles/PMC9319965/ /pubmed/35890857 http://dx.doi.org/10.3390/s22145180 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Sánchez-Brizuela, Guillermo Santos-Criado, Francisco-Javier Sanz-Gobernado, Daniel de la Fuente-López, Eusebio Fraile, Juan-Carlos Pérez-Turiel, Javier Cisnal, Ana Gauze Detection and Segmentation in Minimally Invasive Surgery Video Using Convolutional Neural Networks |
title | Gauze Detection and Segmentation in Minimally Invasive Surgery Video Using Convolutional Neural Networks |
title_full | Gauze Detection and Segmentation in Minimally Invasive Surgery Video Using Convolutional Neural Networks |
title_fullStr | Gauze Detection and Segmentation in Minimally Invasive Surgery Video Using Convolutional Neural Networks |
title_full_unstemmed | Gauze Detection and Segmentation in Minimally Invasive Surgery Video Using Convolutional Neural Networks |
title_short | Gauze Detection and Segmentation in Minimally Invasive Surgery Video Using Convolutional Neural Networks |
title_sort | gauze detection and segmentation in minimally invasive surgery video using convolutional neural networks |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9319965/ https://www.ncbi.nlm.nih.gov/pubmed/35890857 http://dx.doi.org/10.3390/s22145180 |
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