Generating Anatomically Accurate Heart Structures via Neural Implicit Fields

Jiancheng Yang, Ekaterina Sedykh, Jason Ken Adhinarta, Hieu Le, Pascal Fua

Abstract: Implicit functions have significantly advanced shape modeling in diverse fields. Yet, their application within medical imaging often overlooks the intricate interrelations among various anatomical structures, a consideration crucial for accurately modeling complex multi-part structures like the heart. This study presents ImHeart, a latent variable model specifically designed to model complex heart structures. Leveraging the power of learnable templates, ImHeart adeptly captures the intricate relationships between multiple heart components using a unified deformation field and introduces an implicit registration technique to manage the pose variability in medical data. Built on WHS3D dataset of 140 refined whole-heart structures, ImHeart delivers superior reconstruction accuracy and anatomical fidelity. Moreover, we demonstrate the ImHeart can significantly improve heart segmentation from multi-center MRI scans through a retraining pipeline, adeptly navigating the domain gaps inherent to such data.