Lai, Khin Wee and Shoaib, Muhammad Ali and Chuah, Joon Huang and Ahmad Nizar, Muhammad Hanif and Anis, Shazia and Ching, Serena Low Woan (2021) Aortic valve segmentation using deep learning. In: 2020 IEEE EMBS Conference on Biomedical Engineering and Sciences, IECBES 2020, 1 - 3 March 2021, Virtual, Langkawi Island.
Full text not available from this repository.Abstract
Aortic stenosis is the most common type of valvular heart disease (VHD), requiring echocardiography examination for diagnosing and monitoring of the patient. Segmentation of the aortic valve is one of the crucial medical tasks as it helps in different cardiac treatments, e.g. in aortic valve replacement. Manual segmentation is tedious and depends upon the expertise of clinicians so automated segmentation of aortic valve is primarily significant. Deep learning is a viable solution for the automatic segmentation of the aortic valve. Unfortunately, there is lacking knowledge in the application of deep learning in echocardiography. This study proposes a deep learning technique to segment the aortic valve. Echocardiography data of 58 patients for training and neural networks evaluation were obtained from National Heart Institute (IJN). Bi-Directional ConvLSTM U-NET (BDCU-Net),and UNet were trained to segment planimetry aortic valve area. BDCU-Net had the Fl-score 91.092%, followed by UNet90.618 degrees A. The results show that BDCU-Net performance is better than U-Net.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Funders: | UNSPECIFIED |
| Uncontrolled Keywords: | Aortic valve; Deep learning; Detection |
| Subjects: | Q Science > QH Natural history > QH301 Biology R Medicine T Technology > T Technology (General) |
| Divisions: | Faculty of Engineering > Biomedical Engineering Department |
| Depositing User: | Ms Zaharah Ramly |
| Date Deposited: | 18 Oct 2023 10:03 |
| Last Modified: | 18 Oct 2023 10:03 |
| URI: | http://eprints.um.edu.my/id/eprint/35403 |
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