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Segmentation and recognition of breast ultrasound images based on an expanded U-Net
This paper establishes a fully automatic real-time image segmentation and recognition system for breast ultrasound intervention robots. It adopts the basic architecture of a U-shaped convolutional network (U-Net), analyses the actual application scenarios of semantic segmentation of breast ultrasoun...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8205136/ https://www.ncbi.nlm.nih.gov/pubmed/34129619 http://dx.doi.org/10.1371/journal.pone.0253202 |
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author | Guo, Yanjun Duan, Xingguang Wang, Chengyi Guo, Huiqin |
author_facet | Guo, Yanjun Duan, Xingguang Wang, Chengyi Guo, Huiqin |
author_sort | Guo, Yanjun |
collection | PubMed |
description | This paper establishes a fully automatic real-time image segmentation and recognition system for breast ultrasound intervention robots. It adopts the basic architecture of a U-shaped convolutional network (U-Net), analyses the actual application scenarios of semantic segmentation of breast ultrasound images, and adds dropout layers to the U-Net architecture to reduce the redundancy in texture details and prevent overfitting. The main innovation of this paper is proposing an expanded training approach to obtain an expanded of U-Net. The output map of the expanded U-Net can retain texture details and edge features of breast tumours. Using the grey-level probability labels to train the U-Net is faster than using ordinary labels. The average Dice coefficient (standard deviation) and the average IOU coefficient (standard deviation) are 90.5% (±0.02) and 82.7% (±0.02), respectively, when using the expanded training approach. The Dice coefficient of the expanded U-Net is 7.6 larger than that of a general U-Net, and the IOU coefficient of the expanded U-Net is 11 larger than that of the general U-Net. The context of breast ultrasound images can be extracted, and texture details and edge features of tumours can be retained by the expanded U-Net. Using an expanded U-Net can quickly and automatically achieve precise segmentation and multi-class recognition of breast ultrasound images. |
format | Online Article Text |
id | pubmed-8205136 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-82051362021-06-29 Segmentation and recognition of breast ultrasound images based on an expanded U-Net Guo, Yanjun Duan, Xingguang Wang, Chengyi Guo, Huiqin PLoS One Research Article This paper establishes a fully automatic real-time image segmentation and recognition system for breast ultrasound intervention robots. It adopts the basic architecture of a U-shaped convolutional network (U-Net), analyses the actual application scenarios of semantic segmentation of breast ultrasound images, and adds dropout layers to the U-Net architecture to reduce the redundancy in texture details and prevent overfitting. The main innovation of this paper is proposing an expanded training approach to obtain an expanded of U-Net. The output map of the expanded U-Net can retain texture details and edge features of breast tumours. Using the grey-level probability labels to train the U-Net is faster than using ordinary labels. The average Dice coefficient (standard deviation) and the average IOU coefficient (standard deviation) are 90.5% (±0.02) and 82.7% (±0.02), respectively, when using the expanded training approach. The Dice coefficient of the expanded U-Net is 7.6 larger than that of a general U-Net, and the IOU coefficient of the expanded U-Net is 11 larger than that of the general U-Net. The context of breast ultrasound images can be extracted, and texture details and edge features of tumours can be retained by the expanded U-Net. Using an expanded U-Net can quickly and automatically achieve precise segmentation and multi-class recognition of breast ultrasound images. Public Library of Science 2021-06-15 /pmc/articles/PMC8205136/ /pubmed/34129619 http://dx.doi.org/10.1371/journal.pone.0253202 Text en © 2021 Guo et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Guo, Yanjun Duan, Xingguang Wang, Chengyi Guo, Huiqin Segmentation and recognition of breast ultrasound images based on an expanded U-Net |
title | Segmentation and recognition of breast ultrasound images based on an expanded U-Net |
title_full | Segmentation and recognition of breast ultrasound images based on an expanded U-Net |
title_fullStr | Segmentation and recognition of breast ultrasound images based on an expanded U-Net |
title_full_unstemmed | Segmentation and recognition of breast ultrasound images based on an expanded U-Net |
title_short | Segmentation and recognition of breast ultrasound images based on an expanded U-Net |
title_sort | segmentation and recognition of breast ultrasound images based on an expanded u-net |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8205136/ https://www.ncbi.nlm.nih.gov/pubmed/34129619 http://dx.doi.org/10.1371/journal.pone.0253202 |
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