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:需要从人口角度来识别神经系统或精神紊乱的生物标志。 困难来自这些折叠的形态和空间组织上的巨大个体间差异。 这项任务在方法和概念层面都具有挑战性。 在广泛使用的基于注册的技术中,这些差异被视为噪音,而折叠的匹配只是隐含的。 替代方法的基础是提取和明确识别皮层折叠。 特别是, 代表皮层折叠模式, 代表以螺旋盆地或精神紊乱的图示形式将任务正规化为图形匹配问题。 在本文中, 我们提议用多面匹配技术解决在人口层面直接匹配的脉冲图问题。 首先, 我们推动多面匹配框架在此背景下的相关性。 然后我们引入一个程序, 生成人工皮层折叠图形群, 从而使我们能够将多面匹配方法的若干状态作为基准。 我们在人造和真实数据匹配技术上展示了多面匹配技术的有效性, 以便获得从人口角度一致的折叠式盆地标签。