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Automatic segmentation of ovarian follicles using deep neural network combined with edge information

Medical ultrasound imaging plays an important role in computer-aided diagnosis systems. In many cases, it is the preferred method of doctors for diagnosing diseases. Combined with computer vision technology, segmentation of ovarian ultrasound images can help doctors accurately judge diseases, reduce...

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Autores principales: Chen, Zhong, Zhang, Changheng, Li, Zhou, Yang, Jinkun, Deng, He
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9580824/
https://www.ncbi.nlm.nih.gov/pubmed/36303627
http://dx.doi.org/10.3389/frph.2022.877216
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author Chen, Zhong
Zhang, Changheng
Li, Zhou
Yang, Jinkun
Deng, He
author_facet Chen, Zhong
Zhang, Changheng
Li, Zhou
Yang, Jinkun
Deng, He
author_sort Chen, Zhong
collection PubMed
description Medical ultrasound imaging plays an important role in computer-aided diagnosis systems. In many cases, it is the preferred method of doctors for diagnosing diseases. Combined with computer vision technology, segmentation of ovarian ultrasound images can help doctors accurately judge diseases, reduce doctors' workload, and improve doctors' work efficiency. However, accurate segmentation of an ovarian ultrasound image is a challenging task. On the one hand, there is a lot of speckle noise in ultrasound images; on the other hand, the edges of objects are blurred in ultrasound images. In order to segment the target accurately, we propose an ovarian follicles segmentation network combined with edge information. By adding an edge detection branch at the end of the network and taking the edge detection results as one of the losses of the network, we can accurately segment the ovarian follicles in an ultrasound image, making the segmentation results finer on the edge. Experiments show that the proposed network improves the segmentation accuracy of ovarian follicles, and that it has advantages over current algorithms.
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spelling pubmed-95808242022-10-26 Automatic segmentation of ovarian follicles using deep neural network combined with edge information Chen, Zhong Zhang, Changheng Li, Zhou Yang, Jinkun Deng, He Front Reprod Health Reproductive Health Medical ultrasound imaging plays an important role in computer-aided diagnosis systems. In many cases, it is the preferred method of doctors for diagnosing diseases. Combined with computer vision technology, segmentation of ovarian ultrasound images can help doctors accurately judge diseases, reduce doctors' workload, and improve doctors' work efficiency. However, accurate segmentation of an ovarian ultrasound image is a challenging task. On the one hand, there is a lot of speckle noise in ultrasound images; on the other hand, the edges of objects are blurred in ultrasound images. In order to segment the target accurately, we propose an ovarian follicles segmentation network combined with edge information. By adding an edge detection branch at the end of the network and taking the edge detection results as one of the losses of the network, we can accurately segment the ovarian follicles in an ultrasound image, making the segmentation results finer on the edge. Experiments show that the proposed network improves the segmentation accuracy of ovarian follicles, and that it has advantages over current algorithms. Frontiers Media S.A. 2022-08-22 /pmc/articles/PMC9580824/ /pubmed/36303627 http://dx.doi.org/10.3389/frph.2022.877216 Text en Copyright © 2022 Chen, Zhang, Li, Yang and Deng. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Reproductive Health
Chen, Zhong
Zhang, Changheng
Li, Zhou
Yang, Jinkun
Deng, He
Automatic segmentation of ovarian follicles using deep neural network combined with edge information
title Automatic segmentation of ovarian follicles using deep neural network combined with edge information
title_full Automatic segmentation of ovarian follicles using deep neural network combined with edge information
title_fullStr Automatic segmentation of ovarian follicles using deep neural network combined with edge information
title_full_unstemmed Automatic segmentation of ovarian follicles using deep neural network combined with edge information
title_short Automatic segmentation of ovarian follicles using deep neural network combined with edge information
title_sort automatic segmentation of ovarian follicles using deep neural network combined with edge information
topic Reproductive Health
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9580824/
https://www.ncbi.nlm.nih.gov/pubmed/36303627
http://dx.doi.org/10.3389/frph.2022.877216
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