Generation Network for Echocardiographic Sectional Positioning and Shape Completion

Wenli Dai, Hao Xu, Shijie Wang, xiaorong Chen, Dexing Kong

Keywords: Echocardiography; 3D Cardiac Modeling; Weakly Supervised Learning; Point Cloud Generation; AI-assisted Echocardiographic Analysis

TL;DR: Single View Generation Network

Abstract: The precise localization of 2D echocardiography planes in relation to a dynamic heart necessitates specialized expertise, as existing automated algorithms prmarily classify standard views while lacking the capability for comprehensive 3D structural perception. Traditional measurement techniques have evolved to infer 3D heart geometry, yet recent advancements in artificial intelligence, though demonstrating spatial awareness, still fall short in providing explicit 3D modeling. CTA-based digital twins, while promising, are hindered by cost and radiation concerns. Echocardiography, being cost-effective and radiation-free, remains limited in its ability to provide 3D perception. To address this gap, we introduce a novel point cloud-based weakly supervised 3D generation network specifically tailored for echocardiograms. This network automates 3D heart inference, and biomarker modeling, based on 2D echocardiography, slice tracking. To further enhance accuracy, we integrated a self-supervised learning branch into our framework, introducing multi-structure reconstruction loss and an overall reconstruction loss specifically designed for cardiac structure completion. Additionally, we constructed a comparative branch that serves to bolster the network's precision in inferring cardiac structures, thereby refining our approach and elevating the fidelity of the generated 3D models. Our approach enables real-time, robust 3D heart modeling, independent of paired data requirements, thereby facilitating research advancements in echocardiographic digital twins.

Primary Area: other topics in machine learning (i.e., none of the above)

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Submission Number: 10557