论文标题
使用多层感知器和Lipschitz正则化的计算解剖图集
Computational anatomy atlas using multilayer perceptron with Lipschitz regularization
论文作者
论文摘要
计算解剖图集是一组内器官几何形状。它基于真实患者的数据，并通过使用某种数值方法来补充虚拟病例。计算生理学的需求是，尤其是心脏病和神经生理应用。通常，Atlas生成使用明确的对象表示，例如体素模型或表面网格。在本文中，我们提出了一种使用3D对象的隐式表示的方法来生成地图集。我们的方法有两个关键阶段。第一阶段将分段器官的体素模型转换为使用常规多层感知器的隐式形式。此阶段使模型平滑并减少内存消耗。第二阶段使用具有Lipschitz正则化的多层感知器。该神经网络在隐式定义的3D几何形状之间提供了平稳的过渡。我们的工作显示了左右人心室模型的例子。这项工作的所有代码和数据都是打开的。
A computational anatomy atlas is a set of internal organ geometries. It is based on data of real patients and complemented with virtual cases by using a some numerical approach. Atlases are in demand in computational physiology, especially in cardiological and neurophysiological applications. Usually, atlas generation uses explicit object representation, such as voxel models or surface meshes. In this paper, we propose a method of atlas generation using an implicit representation of 3D objects. Our approach has two key stages. The first stage converts voxel models of segmented organs to implicit form using the usual multilayer perceptron. This stage smooths the model and reduces memory consumption. The second stage uses a multilayer perceptron with Lipschitz regularization. This neural network provides a smooth transition between implicitly defined 3D geometries. Our work shows examples of models of the left and right human ventricles. All code and data for this work are open.
