Shoaib, M.A. and Lai, Khin Wee and Khalil, A. and Chuah, Joon Huang (2021) Mask R-CNN for segmentation of left ventricle. In: 3rd International Conference for Innovation in Biomedical Engineering and Life Sciences, ICIBEL 2020, 6 - 7 December 2019, Kuala Lumpur.
Full text not available from this repository.Abstract
Globally, cardiovascular diseases (CVDs) remain the major cause of death among citizens. With echocardiography, doctors are able to diagnose and determine vital parameters for the evaluation of these diseases. Segmentation of left ventricular (LV) from echocardiography is a significant tool for cardiovascular medical analysis. Besides calculating important clinical indices (e.g. ejection fraction), segmentation also can be useful for the investigation of the basic structure of ventricle. Automatic segmentation of the LV has become a valuable means in echocardiography as we can achieve fast and accurate results and a large number of cases can be handled with limited availability of experts. The Convolutional Neural Networks (CNN) have shown outstanding outcomes for image classification, detection, and segmentation in numerous fields. Recently Mask Regions Convolutional Neural Network (Mask R-CNN) has emerged as a very good segmentation model. In this work, Mask R-CNN is proposed for the segmentation of LV. The Mask R-CNN model is first fine-tuned with Common Object in Context (COCO) weights and then the model is trained with our own data. The model first finds out the region of interest (ROI) in the image that contains the desired object i.e. LV. In the ROI, the model segment LV by generating the mask around it. The results demonstrated by the proposed method segments the LV accurately and efficiently with limited training data. © 2021, Springer Nature Switzerland AG.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Funders: | Ministry of Higher Education, Malaysia |
| Uncontrolled Keywords: | Deep learning; Left ventricle; Medical images; Segmentation |
| Subjects: | R Medicine > R Medicine (General) T Technology > TA Engineering (General). Civil engineering (General) |
| Divisions: | Faculty of Engineering > Department of Biomedical Engineering Faculty of Engineering > Department of Electrical Engineering |
| Depositing User: | Ms Zaharah Ramly |
| Date Deposited: | 03 Jul 2025 02:24 |
| Last Modified: | 03 Jul 2025 02:24 |
| URI: | http://eprints.um.edu.my/id/eprint/35811 |
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