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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...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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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. |
format | Online Article Text |
id | pubmed-9580824 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
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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