# Convolutional Neural Network on Semi-Regular Triangulated Meshes and its Application to Brain Image Data

## TL;DR

This paper introduces a novel convolutional neural network designed for semi-regular triangulated meshes, enabling effective analysis of brain MRI data for disease classification, with improved spatial convolution operations.

## Contribution

The paper presents a vertex-based CNN on semi-regular meshes with directly defined convolution and down-sampling, tailored for 3D brain imaging data analysis.

## Key findings

- Effective classification of MCI and AD using the proposed CNN.
- Comparison shows improved performance over spectral graph CNN.
- Demonstrated applicability on large MRI dataset from ADNI.

## Abstract

We developed a convolution neural network (CNN) on semi-regular triangulated meshes whose vertices have 6 neighbours. The key blocks of the proposed CNN, including convolution and down-sampling, are directly defined in a vertex domain. By exploiting the ordering property of semi-regular meshes, the convolution is defined on a vertex domain with strong motivation from the spatial definition of classic convolution. Moreover, the down-sampling of a semi-regular mesh embedded in a 3D Euclidean space can achieve a down-sampling rate of 4, 16, 64, etc. We demonstrated the use of this vertex-based graph CNN for the classification of mild cognitive impairment (MCI) and Alzheimer's disease (AD) based on 3169 MRI scans of the Alzheimer's Disease Neuroimaging Initiative (ADNI). We compared the performance of the vertex-based graph CNN with that of the spectral graph CNN.