Robust Atlas-Based Brain Segmentation Using Multi-structure Confidence-Weighted Registration

We present a robust and accurate atlas-based brain segmentation method which uses multiple initial structure segmentations to simultaneously drive the image registration and achieve anatomically constrained correspondence. We also derive segmentation confidence maps (SCMs) from a given manually segmented training set; these characterize the accuracy of a given set of segmentations as compared to manual segmentations. We incorporate these in our cost term to weight the influence of initial segmentations in the multi-structure registration, such that low confidence regions are given lower weight in the registration. To account for correspondence errors in the underlying registration, we use a supervised atlas correction technique and present a method for correcting the atlas segmentation to account for possible errors in the underlying registration. We applied our multi-structure atlas-based segmentation and supervised atlas correction to segment the amygdala in a set of 23 autistic patients and controls using leave-one-out cross validation, achieving a Dice overlap score of 0.84. We also applied our method to eight subcortical structures in MRI from the Internet Brain Segmentation Repository, with results better or comparable to competing methods.