# Deep Multi-Structural Shape Analysis: Application to Neuroanatomy

arXiv: https://arxiv.org/abs/1806.01069 · HTML: https://mlsanity.com/paper/1806.01069/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Jun 2018.

## Built on by (most cited first, 4 of 4)

- [Medical Application of Geometric Deep Learning for the Diagnosis of Glaucoma](https://mlsanity.com/paper/2204.07004/) (2022)
- [Interpretability of Machine Learning Methods Applied to Neuroimaging](https://mlsanity.com/paper/2204.07005/) (2022)
- [Geometric Deep Learning on Anatomical Meshes for the Prediction of Alzheimer's Disease](https://mlsanity.com/paper/2104.10047/) (2021)
- [Scalable, Axiomatic Explanations of Deep Alzheimer's Diagnosis from Heterogeneous Data](https://mlsanity.com/paper/2107.05997/) (2021)

## Abstract (arXiv)

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.