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Consistency Clustering: A Robust Algorithm for Group-wise Registration, Segmentation and Automatic Atlas Construction in Diffusion MRI
Institution: |
1Computer Science and Artificial Intelligence Lab, MIT, Cambridge, MA, USA 2Laboratory of Mathematics in Imaging, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA |
Publisher: |
Int j Comput Vis |
Publication Date: |
Dec-2009 |
Citation: |
Int j Comput Vis 2009; 85(3):279-290. |
Keywords: |
DTI, Anatomical atlas, Clustering, Segmentation, Tractography, Diffusion imaging, White matter atlas |
Appears in Collections: |
NAC, LMI, NA-MIC, NCIGT |
Sponsors: |
NIH NAMIC U54 EB005149 NIH NAC P41 RR13218 NIH R01 MH074794 NIH R01 NS051826 NIH U41 RR019703 |
Generated Citation: |
Ziyan U, Sabuncu M, Grimson W, Westin C. Consistency Clustering: A Robust Algorithm for Group-wise Registration, Segmentation and Automatic Atlas Construction in Diffusion MRI . Int j Comput Vis 2009; 85(3):279-290. |
| Export citation: |
We propose an integrated registration and clustering algorithm, called “consistency clustering”, that automatically constructs a probabilistic white-matter atlas from a set of multi-subject diffusion weighted MR images. We formulate the atlas creation as a maximum likelihood problem which the proposed method solves using a generalized Expectation Maximization (EM) framework. Additionally, the algorithm employs an outlier rejection and denoising strategy to produce sharp probabilistic maps of certain bundles of interest. We test this algorithm on synthetic and real data, and evaluate its stability against initialization. We demonstrate labeling a novel subject using the resulting spatial atlas and evaluate the accuracy of this labeling. Consistency clustering is a viable tool for completely automatic white-matter atlas construction for sub-populations and the resulting atlas is potentially useful for making diffusion measurements in a common coordinate system to identify pathology related changes or developmental trends.