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 post-processing and cleanup.

翻译：我们提出了一种深度学习模型，能够从患者影像数据自动生成人体心脏的计算机模型，特别强调其生成薄壁心脏结构的能力。该方法通过变形模板网格使其契合给定影像中的心脏结构。与先前采用此方法的深度学习方法相比，我们的框架旨在最小化网格自穿透现象——该问题通常出现在变形间距较小的表面网格时。我们通过使用两阶段微分同胚变形过程，并结合从运动学导出的新型损失函数来实现这一目标，该损失函数可惩罚表面接触与相互穿透。我们的模型在达到与最先进方法相当的精度的同时，还能生成无自交的网格。所得网格可直接用于基于物理的仿真，极大减少了后处理与清理的需求。