We propose a deep neural network for supervised learning on neuroanatomical
shapes. The network directly operates on raw point clouds without the need for
mesh processing or the identification of point correspondences, as spatial
transformer networks map the data to a canonical space. Instead of relying on
hand-crafted shape descriptors, an optimal representation is learned in the
end-to-end training stage of the network. The proposed network consists of
multiple branches, so that features for multiple structures are learned
simultaneously. We demonstrate the performance of our method on two
applications: (i) the prediction of Alzheimer's disease and mild cognitive
impairment and (ii) the regression of the brain age. Finally, we visualize the
important parts of the anatomy for the prediction by adapting the occlusion
method to point clouds.

提出一种基于深度神经网络的神经解剖学形态学的有监督学习方法，直接对原始点云进行操作，通过空间变换网络将数据映射到规范空间，并通过网络的端对端训练阶段学习最优表示，通过多个分支来学习多个结构的特征，应用于阿尔茨海默病和轻度认知障碍的预测和脑龄的回归，最后通过适应点云遮挡法来可视化预测解剖重要部位。