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Found 5 papers, 3 papers with code
SDF4CHD: Generative Modeling of Cardiac Anatomies with Congenital Heart Defects
Our approach has the potential to augment the image-segmentation pairs for rarer CHD types for cardiac segmentation and generate cohorts of CHD cardiac meshes for computational simulation.
LinFlo-Net: A two-stage deep learning method to generate simulation ready meshes of the heart
Our method works by deforming a template mesh to fit the cardiac structures to the given image.
Learning Whole Heart Mesh Generation From Patient Images For Computational Simulations
When evaluated on time-series CT data, this method produced more anatomically and temporally consistent geometries than prior methods, and was able to produce geometries that better satisfy modeling requirements for cardiac flow simulations.
Whole Heart Mesh Generation For Image-Based Computational Simulations By Learning Free-From Deformations
Image-based computer simulation of cardiac function can be used to probe the mechanisms of (patho)physiology, and guide diagnosis and personalized treatment of cardiac diseases.
A Deep-Learning Approach For Direct Whole-Heart Mesh Reconstruction
Furthermore, by deforming a template mesh, our method can generate whole heart geometries with better anatomical consistency and produce high-resolution geometries from lower resolution input image data.
