bibtext: @InProceedings{ Yan_Generating_MICCAI2024,
                   author = { Yang, Jiancheng and Sedykh, Ekaterina and Adhinarta, Jason Ken and Le, Hieu and Fua, Pascal },
                   title = { { Generating Anatomically Accurate Heart Structures via Neural Implicit Fields } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15001 },
                   month = {October},
                   pages = { pending },
                }
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 nuanced 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.
title: Generating Anatomically Accurate Heart Structures via Neural Implicit Fields
authors: Yang, Jiancheng Sedykh, Ekaterina Adhinarta, Jason Ken Le, Hieu Fua, Pascal