Assessing the importance of sex and disease-specific anatomy in electrophysiology and mechanical simulations with a newly developed public virtual cohort of four-chamber heart models
Fig 2
Multi-stage segmentation process.
CemrgApp uses the deep learning model developed by [29] to segment the heart. (a) The output of the U-Net model, which identifies 10 regions, or “labels”, as described in the text. (b) The output of the intermediate stage, where the user manually splits the pulmonary veins into superior and inferior (marked by contrasting colours). (c) The final output of the post-processing stage, where the myocardium of the different structures is extracted, increasing the number of labels to 37. The final segmented images are then upsampled to an isotropic resolution of 0.1mm and smoothed.