Abstract. 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.
In REF , the atlas-based segmentation method presented uses segmentation confidence maps, which are learned from a small manually-segmented training set, and incorporated into the cost term.
Robust atlas-based brain segmentation using multi-structure confidence-weighted registration
Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
Computer Science Medicine