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Comparative studies of deep learning segmentation models for left ventricle segmentation
One of the primary factors contributing to death across all age groups is cardiovascular disease. In the analysis of heart function, analyzing the left ventricle (LV) from 2D echocardiographic images is a common medical procedure for heart patients. Consistent and accurate segmentation of the LV exe...
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/PMC9453312/ https://www.ncbi.nlm.nih.gov/pubmed/36091529 http://dx.doi.org/10.3389/fpubh.2022.981019 |
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author | Shoaib, Muhammad Ali Lai, Khin Wee Chuah, Joon Huang Hum, Yan Chai Ali, Raza Dhanalakshmi, Samiappan Wang, Huanhuan Wu, Xiang |
author_facet | Shoaib, Muhammad Ali Lai, Khin Wee Chuah, Joon Huang Hum, Yan Chai Ali, Raza Dhanalakshmi, Samiappan Wang, Huanhuan Wu, Xiang |
author_sort | Shoaib, Muhammad Ali |
collection | PubMed |
description | One of the primary factors contributing to death across all age groups is cardiovascular disease. In the analysis of heart function, analyzing the left ventricle (LV) from 2D echocardiographic images is a common medical procedure for heart patients. Consistent and accurate segmentation of the LV exerts significant impact on the understanding of the normal anatomy of the heart, as well as the ability to distinguish the aberrant or diseased structure of the heart. Therefore, LV segmentation is an important and critical task in medical practice, and automated LV segmentation is a pressing need. The deep learning models have been utilized in research for automatic LV segmentation. In this work, three cutting-edge convolutional neural network architectures (SegNet, Fully Convolutional Network, and Mask R-CNN) are designed and implemented to segment the LV. In addition, an echocardiography image dataset is generated, and the amount of training data is gradually increased to measure segmentation performance using evaluation metrics. The pixel's accuracy, precision, recall, specificity, Jaccard index, and dice similarity coefficients are applied to evaluate the three models. The Mask R-CNN model outperformed the other two models in these evaluation metrics. As a result, the Mask R-CNN model is used in this study to examine the effect of training data. For 4,000 images, the network achieved 92.21% DSC value, 85.55% Jaccard index, 98.76% mean accuracy, 96.81% recall, 93.15% precision, and 96.58% specificity value. Relatively, the Mask R-CNN outperformed other architectures, and the performance achieves stability when the model is trained using more than 4,000 training images. |
format | Online Article Text |
id | pubmed-9453312 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-94533122022-09-09 Comparative studies of deep learning segmentation models for left ventricle segmentation Shoaib, Muhammad Ali Lai, Khin Wee Chuah, Joon Huang Hum, Yan Chai Ali, Raza Dhanalakshmi, Samiappan Wang, Huanhuan Wu, Xiang Front Public Health Public Health One of the primary factors contributing to death across all age groups is cardiovascular disease. In the analysis of heart function, analyzing the left ventricle (LV) from 2D echocardiographic images is a common medical procedure for heart patients. Consistent and accurate segmentation of the LV exerts significant impact on the understanding of the normal anatomy of the heart, as well as the ability to distinguish the aberrant or diseased structure of the heart. Therefore, LV segmentation is an important and critical task in medical practice, and automated LV segmentation is a pressing need. The deep learning models have been utilized in research for automatic LV segmentation. In this work, three cutting-edge convolutional neural network architectures (SegNet, Fully Convolutional Network, and Mask R-CNN) are designed and implemented to segment the LV. In addition, an echocardiography image dataset is generated, and the amount of training data is gradually increased to measure segmentation performance using evaluation metrics. The pixel's accuracy, precision, recall, specificity, Jaccard index, and dice similarity coefficients are applied to evaluate the three models. The Mask R-CNN model outperformed the other two models in these evaluation metrics. As a result, the Mask R-CNN model is used in this study to examine the effect of training data. For 4,000 images, the network achieved 92.21% DSC value, 85.55% Jaccard index, 98.76% mean accuracy, 96.81% recall, 93.15% precision, and 96.58% specificity value. Relatively, the Mask R-CNN outperformed other architectures, and the performance achieves stability when the model is trained using more than 4,000 training images. Frontiers Media S.A. 2022-08-25 /pmc/articles/PMC9453312/ /pubmed/36091529 http://dx.doi.org/10.3389/fpubh.2022.981019 Text en Copyright © 2022 Shoaib, Lai, Chuah, Hum, Ali, Dhanalakshmi, Wang and Wu. 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 | Public Health Shoaib, Muhammad Ali Lai, Khin Wee Chuah, Joon Huang Hum, Yan Chai Ali, Raza Dhanalakshmi, Samiappan Wang, Huanhuan Wu, Xiang Comparative studies of deep learning segmentation models for left ventricle segmentation |
title | Comparative studies of deep learning segmentation models for left ventricle segmentation |
title_full | Comparative studies of deep learning segmentation models for left ventricle segmentation |
title_fullStr | Comparative studies of deep learning segmentation models for left ventricle segmentation |
title_full_unstemmed | Comparative studies of deep learning segmentation models for left ventricle segmentation |
title_short | Comparative studies of deep learning segmentation models for left ventricle segmentation |
title_sort | comparative studies of deep learning segmentation models for left ventricle segmentation |
topic | Public Health |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9453312/ https://www.ncbi.nlm.nih.gov/pubmed/36091529 http://dx.doi.org/10.3389/fpubh.2022.981019 |
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