Generating Anatomically Accurate Heart Structures via Neural Implicit Fields

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 multicenter MRI scans through a retraining pipeline, adeptly navigating the domain gaps inherent to such data.