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We propose a novel neural deformable model (NDM) targeting at thereconstruction and modeling of 3D bi-ventricular shape of the heart from 2Dsparse cardiac magnetic resonance (CMR) imaging data. We model thebi-ventricular shape using blended deformable superquadrics, which areparameterized by a set of geometric parameter functions and are capable ofdeforming globally and locally. While global geometric parameter functions anddeformations capture gross shape features from visual data, local deformations,parameterized as neural diffeomorphic point flows, can be learned to recoverthe detailed heart shape.Different from iterative optimization methods used inconventional deformable model formulations, NDMs can be trained to learn suchgeometric parameter functions, global and local deformations from a shapedistribution manifold. Our NDM can learn to densify a sparse cardiac pointcloud with arbitrary scales and generate high-quality triangular meshesautomatically. It also enables the implicit learning of dense correspondencesamong different heart shape instances for accurate cardiac shape registration.Furthermore, the parameters of NDM are intuitive, and can be used by aphysician without sophisticated post-processing. Experimental results on alarge CMR dataset demonstrate the improved performance of NDM over conventionalmethods.

Neural Deformable Models for 3D Bi-Ventricular Heart Shape Reconstruction and Modeling from 2D Sparse Cardiac Magnetic Resonance Imaging