Comparison of two-dimensional and three-dimensional U-Net architectures for segmentation of adipose tissue in cardiac magnetic resonance images.
The process of identifying cardiac adipose tissue (CAT) from volumetric magnetic resonance imaging of the heart is tedious, time-consuming, and often dependent on observer interpretation. Many 2-dimensional (2D) convolutional neural networks (CNNs) have been implemented to automate the cardiac segme...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 60; no. 8; pp. 2291 - 2307 |
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| Autores principales: | , , , |
| Formato: | research Journal Article |
| Publicado: |
Springer Nature
Aug2022
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| Acceso en línea: | Ver este registro en EBSCOhost |