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.

翻译：脑沟回结构的群体匹配对于识别神经系统或精神疾病的生物标志物至关重要。其困难源于脑沟形态及空间组织结构的巨大个体差异，这给方法论和概念层面都带来了挑战。在广泛使用的基于配准的技术中，这些差异被视为噪声，而脑沟的匹配仅以隐式方式进行。替代方法则基于脑沟回结构的提取与显式识别。特别是，将皮层折叠模式表示为脑沟基底图（称为脑沟图）能够将该任务形式化为图匹配问题。本文提出直接采用多图匹配技术从群体层面解决脑沟图匹配问题。首先，我们论证了多图匹配框架在此背景下的适用性；继而引入了一种人工脑沟图群体生成流程，从而能够对多种主流多图匹配方法进行基准测试。在人工数据与真实数据上的实验结果均表明，多图匹配技术可在脑沟基底层面实现皮层折叠的群体一致性标注。