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.

我们提出了一种深度学习模型，可以根据患者的影像数据自动生成人类心脏的计算机模型，特别强调其生成薄壁心脏结构的能力。

LinFlo-Net: 心脏模拟准备网格生成的两阶段深度学习方法

LinFlo-Net: A two-stage deep learning method to generate simulation  ready meshes of the heart

Accurate geometric quantification of the human heart is a key step in the
diagnosis of numerous cardiac diseases, and in the management of cardiac
patients. Ultrasound imaging is the primary modality for cardiac imaging,
however acquisition requires high operator skill, and its interpretation and
analysis is difficult due to artifacts. Reconstructing cardiac anatomy in 3D
can enable discovery of new biomarkers and make imaging less dependent on
operator expertise, however most ultrasound systems only have 2D imaging
capabilities. We propose both a simple alteration to the Pix2Vox++ networks for
a sizeable reduction in memory usage and computational complexity, and a
pipeline to perform reconstruction of 3D anatomy from 2D standard cardiac
views, effectively enabling 3D anatomical reconstruction from limited 2D data.
We evaluate our pipeline using synthetically generated data achieving accurate
3D whole-heart reconstructions (peak intersection over union score > 0.88) from
just two standard anatomical 2D views of the heart. We also show preliminary
results using real echo images.

通过对 Pix2Vox++ 网络的简单改进和一种 2D 标准心脏视图重建 3D 解剖结构的流程，实现了在有限的 2D 数据情况下的 3D 解剖结构重建，并据此发现新的生物标志物实现对人类心脏的精确几何量化。