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

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 aut...

• Type: Conference
• Year: 2009
• Where: MICCAI
• Authors: Ali R. Khan, Moo K. Chung, Mirza Faisal Beg
• Keywords: Brain Segmentation Method, Initial Structure Segmentations, Medical Imaging, MICCAI 2009, Segmentation Confidence Maps