Effect of inter-subject variation on the accuracy of atlas-based segmentation applied to human brain structures

Abstract

Large variations occur in brain anatomical structures in human populations, presenting a critical challenge to the brain mapping process. This study investigates the major impact of these variations on the performance of atlas-based segmentation. It is based on two publicly available datasets, from each of which 17 T1-weighted brain atlases were extracted. Each subject was registered to every other subject using the Morphons, a non-rigid registration algorithm. The automatic segmentations, obtained by warping the segmentation of this template, were compared with the expert segmentations using Dice index and the differences were statistically analyzed using Bonferroni multiple comparisons at significance level 0.05. The results showed that an optimum atlas for accurate segmentation of all structures cannot be found, and that the group of preferred templates, defined as being significantly superior to at least two other templates regarding the segmentation accuracy, varies significantly from structure to structure. Moreover, compared to other templates, a template giving the best accuracy in segmentation of some structures can provide highly inferior segmentation accuracy for other structures. It is concluded that there is no template optimum for automatic segmentation of all anatomical structures in the brain because of high inter-subject variation. Using a single fixed template for brain segmentation does not lead to good overall segmentation accuracy. This proves the need for multiple atlas based solutions in the context of atlas-based segmentation on human brain.