3D High-Resolution Cardiac Segmentation Reconstruction From 2D Views Using Conditional Variational Autoencoders - Biffi, Carlo; Cerrolaza, Juan J.; Tarroni, Giacomo; de Marvao, Antonio; Cook, Stuart A.; O'Regan, Declan P.; Rueckert, Daniel (Proceedings (International Symposium on Biomedical Imaging), 2019)
Accurate segmentation of heart structures imaged by cardiac MR is key for the quantitative analysis of pathology. High-resolution 3D MR sequences enable whole-heart structural imaging but are time-consuming, expensive to acquire and they often require long breath holds that are not suitable for patients. Consequently, multiplanar breath-hold 2D cines sequences are standard practice but are disadvantaged by lack of whole-heart coverage and low through-plane resolution. To address this, we propose a conditional variational autoencoder architecture able to learn a generative model of 3D high-resolution left ventricular (LV) segmentations which is conditioned on three 2D LV segmentations of one short-axis and two long-axis images. By only employing these three 2D segmentations, our model can efficiently reconstruct the 3D high-resolution LV segmentation of a subject. When evaluated on 400 unseen healthy volunteers, our model yielded an average Dice score of 87. 92 ± 0.15 and outperformed competing architectures (TL-net, Dice score =82.60± 0.23, p=2.2· 10^{-16}).