Comparative evaluation of deep learning architectures, including UNet, TransUNet, and MIST, for left atrium segmentation in cardiac computed tomography of congenital heart diseases
Purpose This study compares 3 deep learning models (UNet, TransUNet, and MIST) for left atrium (LA) segmentation of cardiac computed tomography (CT) images from patients with congenital heart disease (CHD). It investigates how architectural variations in the MIST model, such as spatial squeeze-and-e...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Ewha Womans University College of Medicine
2025-04-01
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| Series: | The Ewha Medical Journal |
| Subjects: | |
| Online Access: | http://www.e-emj.org/upload/pdf/emj-2025-00087.pdf |
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