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Abstract #2230

A Machine Learning Approach to Left Ventricle Mesh Prediction from Multi-Slice MR Images

Thomas Joyce1, Stefano Buoso1, Yuerong Xu1, and Sebastian Kozerke1
1Institute for Biomedical Engineering, University and ETH Zurich, Zurich, Switzerland

There has been considerable success applying deep learning to cardiac MRI segmentation. However, segmentation masks have several shortcomings. In particular, they are discrete (voxel-based) representations of continuous anatomy, and are hence not suitable for use in biomechanical simulation. Mesh representations can potentially overcome both of these drawbacks. We demonstrate an approach to predicting left-ventricular (LV) meshes from short-axis MR images. The proposed approach: (i) works robustly on data with differing slice numbers, slice thicknesses, and left-ventricular coverage, (ii) has no test-time optimisation loop, but rather directly predicts the mesh from a mask, and, (iii) generalises to real data with pathology.

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