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Accurate segmentation of heart structures imaged by cardiac MR is key for thequantitative analysis of pathology. High-resolution 3D MR sequences enablewhole-heart structural imaging but are time-consuming, expensive to acquire andthey often require long breath holds that are not suitable for patients.Consequently, multiplanar breath-hold 2D cine sequences are standard practicebut are disadvantaged by lack of whole-heart coverage and low through-planeresolution. To address this, we propose a conditional variational autoencoderarchitecture able to learn a generative model of 3D high-resolution leftventricular (LV) segmentations which is conditioned on three 2D LVsegmentations of one short-axis and two long-axis images. By only employingthese three 2D segmentations, our model can efficiently reconstruct the 3Dhigh-resolution LV segmentation of a subject. When evaluated on 400 unseenhealthy volunteers, our model yielded an average Dice score of $87.92 \pm 0.15$and outperformed competing architectures.

3D High-Resolution Cardiac Segmentation Reconstruction from 2D Views using Conditional Variational Autoencoders