Latent Disentanglement in Mesh Variational Autoencoders Improves the Diagnosis of Craniofacial Syndromes and Aids Surgical Planning

TL;DR

This paper introduces a novel mesh variational autoencoder that disentangles latent features, enabling improved diagnosis of craniofacial syndromes, regional analysis of head morphology, and simulation of surgical outcomes for better planning and evaluation.

Contribution

It presents the first application of latent disentanglement in mesh VAEs for craniofacial shape analysis, diagnosis, and surgical simulation.

Findings

Enhanced syndrome classification accuracy.

Ability to analyze regional influence on syndromic phenotypes.

Simulation of surgical procedures for outcome prediction.

Abstract

The use of deep learning to undertake shape analysis of the complexities of the human head holds great promise. However, there have traditionally been a number of barriers to accurate modelling, especially when operating on both a global and local level. In this work, we will discuss the application of the Swap Disentangled Variational Autoencoder (SD-VAE) with relevance to Crouzon, Apert and Muenke syndromes. Although syndrome classification is performed on the entire mesh, it is also possible, for the first time, to analyse the influence of each region of the head on the syndromic phenotype. By manipulating specific parameters of the generative model, and producing procedure-specific new shapes, it is also possible to simulate the outcome of a range of craniofacial surgical procedures. This opens new avenues to advance diagnosis, aids surgical planning and allows for the objective evaluation of surgical outcomes.