论文标题
从患者图像中学习整个心脏网格的生成计算模拟
Learning Whole Heart Mesh Generation From Patient Images For Computational Simulations
论文作者
论文摘要
患者特异性的心脏建模结合了从医学图像和生物物理模拟中得出的心脏的几何形状，以预测心脏功能的各个方面。但是，从患者图像数据中生成拟合心脏的模拟模型通常需要复杂的程序和大量的人为努力。我们提出了一种快速自动化的深度学习方法，可以从医学图像中构建心脏模拟模型。该方法通过学会在整个心脏模板上变形一组变形手柄，从而从3D患者图像中构建网络。对于3D CT和MR数据，此方法可实现全心重建的有希望的准确性，在构造心脏的模拟拟合网格时始终优于先前方法。当对时间序列CT数据进行评估时，该方法在解剖和时间上的几何形状比先前的方法产生更多的几何形状，并且能够产生能够更好地满足心脏流动模拟建模要求的几何形状。我们的源代码和预估计的网络可在https://github.com/fkong7/heartdeformnets上找到。
Patient-specific cardiac modeling combines geometries of the heart derived from medical images and biophysical simulations to predict various aspects of cardiac function. However, generating simulation-suitable models of the heart from patient image data often requires complicated procedures and significant human effort. We present a fast and automated deep-learning method to construct simulation-suitable models of the heart from medical images. The approach constructs meshes from 3D patient images by learning to deform a small set of deformation handles on a whole heart template. For both 3D CT and MR data, this method achieves promising accuracy for whole heart reconstruction, consistently outperforming prior methods in constructing simulation-suitable meshes of the heart. When evaluated on time-series CT data, this method produced more anatomically and temporally consistent geometries than prior methods, and was able to produce geometries that better satisfy modeling requirements for cardiac flow simulations. Our source code and pretrained networks are available at https://github.com/fkong7/HeartDeformNets.
