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

arXiv: https://arxiv.org/abs/2301.13532 · HTML: https://mlsanity.com/paper/2301.13532/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 104 references that are in the map.
- First posted on arXiv 31 Jan 2023.

## Builds on (most cited first, 17 of 17)

- [How Powerful are Graph Neural Networks?](https://mlsanity.com/paper/1810.00826/) (2018)
- [Matrix Completion and Low-Rank SVD via Fast Alternating Least Squares](https://mlsanity.com/paper/1410.2596/) (2014)
- [Learning Combinatorial Embedding Networks for Deep Graph Matching](https://mlsanity.com/paper/1904.00597/) (2019)
- [Deep Graph Matching Consensus](https://mlsanity.com/paper/2001.09621/) (2020)
- [Neural Graph Matching Network: Learning Lawler's Quadratic Assignment Problem with Extension to Hypergraph and Multiple-graph Matching](https://mlsanity.com/paper/1911.11308/) (2019)
- [Deep Graph Matching via Blackbox Differentiation of Combinatorial Solvers](https://mlsanity.com/paper/2003.11657/) (2020)
- [Multi-Image Matching via Fast Alternating Minimization](https://mlsanity.com/paper/1505.04845/) (2015)
- [Higher-order Projected Power Iterations for Scalable Multi-Matching](https://mlsanity.com/paper/1811.10541/) (2018)
- [Near-Optimal Joint Object Matching via Convex Relaxation](https://mlsanity.com/paper/1402.1473/) (2014)
- [Multi-Image Semantic Matching by Mining Consistent Features](https://mlsanity.com/paper/1711.07641/) (2017)
- [KerGM: Kernelized Graph Matching](https://mlsanity.com/paper/1911.11120/) (2019)
- [A General Multi-Graph Matching Approach via Graduated Consistency-regularized Boosting](https://mlsanity.com/paper/1502.05840/) (2015)
- [Robust Multi-object Matching via Iterative Reweighting of the Graph Connection Laplacian](https://mlsanity.com/paper/2006.06658/) (2020)
- [Quantum Optimal Transport for Tensor Field Processing](https://mlsanity.com/paper/1612.08731/) (2016)
- [A Graph Is More Than Its Nodes: Towards Structured Uncertainty-Aware Learning on Graphs](https://mlsanity.com/paper/2210.15575/) (2022)
- [Kernelized multi-graph matching](https://mlsanity.com/paper/2210.05206/) (2022)
- [Universe Points Representation Learning for Partial Multi-Graph Matching](https://mlsanity.com/paper/2212.00780/) (2022)

## Built on by (most cited first, 1 of 1)

- [Optimizing Incomplete, Large-Scale and Sparse Multi-Graph Matching in Bioimaging](https://mlsanity.com/paper/2406.18215/) (2024)

## Abstract (arXiv)

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.