Benefits of mirror weight symmetry for 3D mesh segmentation in biomedical applications

TL;DR

This paper demonstrates that incorporating weight symmetry in neural networks improves 3D mesh segmentation accuracy and reduces model complexity, especially beneficial for biomedical applications with limited training data.

Contribution

The study shows the positive impact of weight symmetry in neural networks for 3D mesh segmentation, including accuracy gains and parameter reduction, applicable to biomedical structures.

Findings

Weight symmetry increases segmentation accuracy by 1-3%.

It reduces the number of trainable parameters up to 8 times.

Effective even with small training datasets.

Abstract

3D mesh segmentation is an important task with many biomedical applications. The human body has bilateral symmetry and some variations in organ positions. It allows us to expect a positive effect of rotation and inversion invariant layers in convolutional neural networks that perform biomedical segmentations. In this study, we show the impact of weight symmetry in neural networks that perform 3D mesh segmentation. We analyze the problem of 3D mesh segmentation for pathological vessel structures (aneurysms) and conventional anatomical structures (endocardium and epicardium of ventricles). Local geometrical features are encoded as sampling from the signed distance function, and the neural network performs prediction for each mesh node. We show that weight symmetry gains from 1 to 3% of additional accuracy and allows decreasing the number of trainable parameters up to 8 times without suffering the performance loss if neural networks have at least three convolutional layers. This also works for very small training sets.