A Convolutional Neural Network Model to Segment Myocardial Infarction from MRI Images
Cardiovascular diseases (CVDs) are considered one of the leading causes of death worldwide. Myocardial infarction (MI) is one of the deadliest cardiac diseases that require more consideration. Recently, cardiac magnetic resonance imaging (MRI) has been applied as a standard technique for assessing...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
2023
|
| Subjects: | |
| Online Access: | http://eprints.uthm.edu.my/8821/ http://eprints.uthm.edu.my/8821/1/J15859_3c4e6c8af7d98681ad3a232d0007bca9.pdf |
| _version_ | 1848889507093413888 |
|---|---|
| author | Farea Shaaf, Zakarya Abdul Jamil, Muhammad Mahadi Ambar, Radzi |
| author_facet | Farea Shaaf, Zakarya Abdul Jamil, Muhammad Mahadi Ambar, Radzi |
| author_sort | Farea Shaaf, Zakarya |
| building | UTHM Institutional Repository |
| collection | Online Access |
| description | Cardiovascular diseases (CVDs) are considered one of the leading causes of death worldwide. Myocardial infarction (MI) is one of the deadliest cardiac diseases that require more consideration. Recently, cardiac magnetic resonance imaging (MRI) has been applied as a standard technique for assessing
such diseases. The segmentation of the left ventricle (LV) and myocardium from MRI images is vital in detecting MI disease at its early stages. The automatic segmentation of LV is still challenging due to the complex structures of MRI images, inhomogeneous LV shape and moving organs around the LV, such as the lungs and diaphragm. Thus, this study proposed a convolutional neural network (CNN) model for LV and myocardium segmentation to detect MI. The layers selection and hyper-parameters fine-tuning were applied before the training phase. The model showed robust performance based on the evaluation metrics such as accuracy, sensitivity, specificity, dice score coefficient (DSC), Jaccard index and intersection over union (IOU) with values of 0.86, 0.91, 0.84, 0.81, 0.69 and 0.83, respectively. |
| first_indexed | 2025-11-15T20:27:16Z |
| format | Article |
| id | uthm-8821 |
| institution | Universiti Tun Hussein Onn Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T20:27:16Z |
| publishDate | 2023 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | uthm-88212023-06-18T01:29:43Z http://eprints.uthm.edu.my/8821/ A Convolutional Neural Network Model to Segment Myocardial Infarction from MRI Images Farea Shaaf, Zakarya Abdul Jamil, Muhammad Mahadi Ambar, Radzi T Technology (General) Cardiovascular diseases (CVDs) are considered one of the leading causes of death worldwide. Myocardial infarction (MI) is one of the deadliest cardiac diseases that require more consideration. Recently, cardiac magnetic resonance imaging (MRI) has been applied as a standard technique for assessing such diseases. The segmentation of the left ventricle (LV) and myocardium from MRI images is vital in detecting MI disease at its early stages. The automatic segmentation of LV is still challenging due to the complex structures of MRI images, inhomogeneous LV shape and moving organs around the LV, such as the lungs and diaphragm. Thus, this study proposed a convolutional neural network (CNN) model for LV and myocardium segmentation to detect MI. The layers selection and hyper-parameters fine-tuning were applied before the training phase. The model showed robust performance based on the evaluation metrics such as accuracy, sensitivity, specificity, dice score coefficient (DSC), Jaccard index and intersection over union (IOU) with values of 0.86, 0.91, 0.84, 0.81, 0.69 and 0.83, respectively. 2023 Article PeerReviewed text en http://eprints.uthm.edu.my/8821/1/J15859_3c4e6c8af7d98681ad3a232d0007bca9.pdf Farea Shaaf, Zakarya and Abdul Jamil, Muhammad Mahadi and Ambar, Radzi (2023) A Convolutional Neural Network Model to Segment Myocardial Infarction from MRI Images. -, 19 (2). pp. 150-162. https://doi.org/10.3991/ijoe.v19i02.36607 |
| spellingShingle | T Technology (General) Farea Shaaf, Zakarya Abdul Jamil, Muhammad Mahadi Ambar, Radzi A Convolutional Neural Network Model to Segment Myocardial Infarction from MRI Images |
| title | A Convolutional Neural Network Model to Segment Myocardial Infarction from MRI Images |
| title_full | A Convolutional Neural Network Model to Segment Myocardial Infarction from MRI Images |
| title_fullStr | A Convolutional Neural Network Model to Segment Myocardial Infarction from MRI Images |
| title_full_unstemmed | A Convolutional Neural Network Model to Segment Myocardial Infarction from MRI Images |
| title_short | A Convolutional Neural Network Model to Segment Myocardial Infarction from MRI Images |
| title_sort | convolutional neural network model to segment myocardial infarction from mri images |
| topic | T Technology (General) |
| url | http://eprints.uthm.edu.my/8821/ http://eprints.uthm.edu.my/8821/ http://eprints.uthm.edu.my/8821/1/J15859_3c4e6c8af7d98681ad3a232d0007bca9.pdf |