Work

LinFlo-Net: A Two-Stage Deep Learning Method to Generate Simulation Ready Meshes of the Heart

Year: 2024

Type: article

Abstract: We present a deep learning model to automatically generate computer models of the human heart from patient imaging data with an emphasis on its capability to generate thin-walled cardiac structures. Our method works by deforming a template mesh to fit the cardiac structures to the given image. Compared with prior deep learning methods that adopted this approach, our framework is designed to minimize mesh self-penetration, which typically arises when deforming surface meshes separated by small distances. We achieve this by using a two-stage diffeomorphic deformation process along with a novel loss function derived from the kinematics of motion that penalizes surface contact and interpenetration. Our model demonstrates comparable accuracy with state-of-the-art methods while additionally producing meshes free of self-intersections. The resultant meshes are readily usable in physics based simulation, minimizing the need for postprocessing and cleanup.

Source: Journal of Biomechanical Engineering

DOI: 10.1115/1.4064527

Authors: Arjun Narayanan, Fanwei Kong, Shawn Shadden

Institutions: University of California, Berkeley, Stanford Medicine, Stanford University

Language: English

Topic: 3D Shape Modeling and Analysis

Subfield: Computer Graphics and Computer-Aided Design

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: hybrid

Funder: National Science Foundation