Breast MRI Segmentation and Ki-67 High- and Low-Expression Prediction Algorithm Based on Deep Learning

RESULTS: The DSC, PPV, and sensitivity of our combined model are 0.94, 0.93, and 0.94, respectively, with better segmentation performance. And we compare with the segmentation frameworks of other papers and find that our combined model can make accurate segmentation of breast tumors. CONCLUSION: Our...

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Detalles Bibliográficos
Autores principales: Li, Yuan-Zhe, Huang, Yin-Hui, Su, Xian-yan, Gu, Zhen-qi, Lai, Qing-Quan, Huang, Jing, Li, Shu-Ting, Wang, Yi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2022
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9553330/
https://www.ncbi.nlm.nih.gov/pubmed/36238476
http://dx.doi.org/10.1155/2022/1770531
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author Li, Yuan-Zhe
Huang, Yin-Hui
Su, Xian-yan
Gu, Zhen-qi
Lai, Qing-Quan
Huang, Jing
Li, Shu-Ting
Wang, Yi
author_facet Li, Yuan-Zhe
Huang, Yin-Hui
Su, Xian-yan
Gu, Zhen-qi
Lai, Qing-Quan
Huang, Jing
Li, Shu-Ting
Wang, Yi
author_sort Li, Yuan-Zhe
collection PubMed
description RESULTS: The DSC, PPV, and sensitivity of our combined model are 0.94, 0.93, and 0.94, respectively, with better segmentation performance. And we compare with the segmentation frameworks of other papers and find that our combined model can make accurate segmentation of breast tumors. CONCLUSION: Our method can adapt to the variability of breast tumors and segment breast tumors accurately and efficiently. In the future, it can be widely used in clinical practice, so as to help the clinic better formulate a reasonable diagnosis and treatment plan for breast cancer patients.
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spelling pubmed-95533302022-10-12 Breast MRI Segmentation and Ki-67 High- and Low-Expression Prediction Algorithm Based on Deep Learning Li, Yuan-Zhe Huang, Yin-Hui Su, Xian-yan Gu, Zhen-qi Lai, Qing-Quan Huang, Jing Li, Shu-Ting Wang, Yi Comput Math Methods Med Research Article RESULTS: The DSC, PPV, and sensitivity of our combined model are 0.94, 0.93, and 0.94, respectively, with better segmentation performance. And we compare with the segmentation frameworks of other papers and find that our combined model can make accurate segmentation of breast tumors. CONCLUSION: Our method can adapt to the variability of breast tumors and segment breast tumors accurately and efficiently. In the future, it can be widely used in clinical practice, so as to help the clinic better formulate a reasonable diagnosis and treatment plan for breast cancer patients. Hindawi 2022-10-04 /pmc/articles/PMC9553330/ /pubmed/36238476 http://dx.doi.org/10.1155/2022/1770531 Text en Copyright © 2022 Yuan-Zhe Li et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Li, Yuan-Zhe
Huang, Yin-Hui
Su, Xian-yan
Gu, Zhen-qi
Lai, Qing-Quan
Huang, Jing
Li, Shu-Ting
Wang, Yi
Breast MRI Segmentation and Ki-67 High- and Low-Expression Prediction Algorithm Based on Deep Learning
title Breast MRI Segmentation and Ki-67 High- and Low-Expression Prediction Algorithm Based on Deep Learning
title_full Breast MRI Segmentation and Ki-67 High- and Low-Expression Prediction Algorithm Based on Deep Learning
title_fullStr Breast MRI Segmentation and Ki-67 High- and Low-Expression Prediction Algorithm Based on Deep Learning
title_full_unstemmed Breast MRI Segmentation and Ki-67 High- and Low-Expression Prediction Algorithm Based on Deep Learning
title_short Breast MRI Segmentation and Ki-67 High- and Low-Expression Prediction Algorithm Based on Deep Learning
title_sort breast mri segmentation and ki-67 high- and low-expression prediction algorithm based on deep learning
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9553330/
https://www.ncbi.nlm.nih.gov/pubmed/36238476
http://dx.doi.org/10.1155/2022/1770531
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