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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...

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Autores principales: Guo, Yanjun, Duan, Xingguang, Wang, Chengyi, Guo, Huiqin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2021
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.
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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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