Population-wise Labeling of Sulcal Graphs using Multi-graph Matching

TL;DR

This paper introduces a population-level approach to matching cortical sulcal graphs using multi-graph matching techniques, improving consistency in labeling folds across individuals for neurological research.

Contribution

It presents a novel application of multi-graph matching to cortical sulcal graphs, enabling population-wise consistent labeling of brain folds.

Findings

Multi-graph matching improves population-wise labeling accuracy.

Artificial sulcal graph generation aids benchmarking methods.

Results show effectiveness on real and synthetic data.

Abstract

Population-wise matching of the cortical fold is necessary to identify biomarkers of neurological or psychiatric disorders. The difficulty comes from the massive interindividual variations in the morphology and spatial organization of the folds. This task is challenging at both methodological and conceptual levels. In the widely used registration-based techniques, these variations are considered as noise and the matching of folds is only implicit. Alternative approaches are based on the extraction and explicit identification of the cortical folds. In particular, representing cortical folding patterns as graphs of sulcal basins-termed sulcal graphs-enables to formalize the task as a graph-matching problem. In this paper, we propose to address the problem of sulcal graph matching directly at the population level using multi-graph matching techniques. First, we motivate the relevance of multi-graph matching framework in this context. We then introduce a procedure to generate populations of artificial sulcal graphs, which allows us benchmarking several state of the art multi-graph matching methods. Our results on both artificial and real data demonstrate the effectiveness of multi-graph matching techniques to obtain a population-wise consistent labeling of cortical folds at the sulcal basins level.