Shoaib, Muhammad Ali and Lai, Khin Wee and Chuah, Joon Huang and Hum, Yan Chai and Ali, Raza and Dhanalakshmi, Samiappan and Wang, Huanhuan and Wu, Xiang (2022) Comparative studies of deep learning segmentation models for left ventricle segmentation. Frontiers in Public Health, 10. ISSN 2296-2565, DOI https://doi.org/10.3389/fpubh.2022.981019.
Full text not available from this repository.Abstract
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.
| Item Type: | Article |
|---|---|
| Funders: | Ministry of Education, Malaysia, Universiti Malaya [FRGS/1/2019/TK04/UM/01/2] |
| Uncontrolled Keywords: | Convolutional Neural Network (CNN); segmentation; image processing; deep learning; left ventricle (LV); echocardiography |
| Subjects: | T Technology > TK Electrical engineering. Electronics Nuclear engineering |
| Divisions: | Faculty of Engineering > Biomedical Engineering Department Faculty of Engineering > Department of Electrical Engineering |
| Depositing User: | Ms Koh Ai Peng |
| Date Deposited: | 23 Jul 2024 05:07 |
| Last Modified: | 23 Jul 2024 05:07 |
| URI: | http://eprints.um.edu.my/id/eprint/46261 |
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