On The Relevance of Multi-Graph Matching for Sulcal Graphs

Fine-scale characterization of the geometry of the folding patterns of the brain is a key processing step in neuroscience, with high impact applications such as for uncovering biomarkers indicative of a neurological pathology. Sulcal graphs constitute relevant representations of the complex and vari...

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Bibliographic Details
Published in2022 IEEE International Conference on Image Processing (ICIP) pp. 2536 - 2540
Main Authors Yadav, R., Dupe, F. X., Takerkart, S., Auzias, G.
Format Conference Proceeding
LanguageEnglish
Published IEEE 16.10.2022
Subjects
Benchmark testing
Geometry
Image processing
Neuroscience
Pathology
Scalability
Sociology
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Summary:Fine-scale characterization of the geometry of the folding patterns of the brain is a key processing step in neuroscience, with high impact applications such as for uncovering biomarkers indicative of a neurological pathology. Sulcal graphs constitute relevant representations of the complex and variable geometry of the cortex of individual brains. Comparing sulcal graphs is challenging due to variations across subjects in the number of nodes, graph topology and attributes (on both nodes and edges). Graph matching experiments on real data are limited by the absence of ground truth. In this paper we propose to generate synthetic graphs to benchmark graph matching methods and assess their robustness to noise on attributes and to the presence of un-matchable nodes. Three multi-graph matching methods are compared to one pairwise approach in various simulation settings, showing that good matching performances can be achieved even with highly perturbed sulcal graphs. An experiment on real data from a population of 134 subjects further unveil large performance differences across matching methods.
ISSN:2381-8549
DOI:10.1109/ICIP46576.2022.9897185