In this paper we propose a method for whole brain parcellation using the type of generative parametric models typically used in tissue classification. Compared to the non-parametric, multi-atlas segmentation techniques that have become popular in recent years, our method obtains state-of-the-art segmentation performance in both cortical and subcortical structures, while retaining all the benefits of generative parametric models, including high computational speed, automatic adaptiveness to changes in image contrast when different scanner platforms and pulse sequences are used, and the ability to handle multi-contrast (vector-valued intensities) MR data. We have validated our method by comparing its segmentations to manual delineations both within and across scanner platforms and pulse sequences, and show preliminary results on multi-contrast test-retest scans, demonstrating the feasibility of the approach.
Lecture Notes in Computer Science: 16th International Conference, Nagoya, Japan, September 22-26, 2013, Proceedings, Part I, 2013, p. 727-734
Image segmentation; Scanning; Models
Main Research Area:
Lecture Notes in Computer Science
16th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2013)Medical Image Computing and Computer Assisted Intervention