A Soft-Correspondence Approach to Shape-based Disease Grading with Graph Convolutional Networks

Julius Mayer, Daniel Baum, Felix Ambellan, Christoph von Tycowicz

Keywords: Shape Analysis, Classification, Functional Maps, Geometric Deep Learning

TL;DR: We present a graph-based shape classification based on a flexible, yet descriptive characterization of shape variations.

Abstract: Shape analysis provides principled means for understanding anatomical structures from medical images. The underlying notions of shape spaces, however, come with strict assumptions prohibiting the analysis of incomplete and/or topologically varying shapes. This work aims to alleviate these limitations by adapting the concept of soft correspondences. In particular, we present a graph-based learning approach for morphometric classification of disease states that is based on a generalized notion of shape correspondences in terms of functional maps. We demonstrate the performance of the derived classifier on the open-access ADNI database for differentiating normal controls and subjects with Alzheimer’s disease. Notably, our experiment shows that our approach can improve over state-of-the-art from geometric deep learning.