B.C.D., P.G., and B.F. contributed equally to this work.
Automated segmentation of hippocampal subfields from ultra-high resolution in vivo MRI
Article first published online: 29 APR 2009
Copyright © 2009 Wiley-Liss, Inc.
Special Issue: Proceedings for the Computational Hippocampal Anatomy and Physiology Workshop 2008
Volume 19, Issue 6, pages 549–557, June 2009
How to Cite
Van Leemput, K., Bakkour, A., Benner, T., Wiggins, G., Wald, L. L., Augustinack, J., Dickerson, B. C., Golland, P. and Fischl, B. (2009), Automated segmentation of hippocampal subfields from ultra-high resolution in vivo MRI. Hippocampus, 19: 549–557. doi: 10.1002/hipo.20615
- Issue published online: 26 MAY 2009
- Article first published online: 29 APR 2009
- Manuscript Accepted: 9 MAR 2009
- NIH NCRR. Grant Numbers: P41-RR14075, R01 RR16594-01A1, NAC P41-RR13218
- BIRN Morphometric Project. Grant Numbers: BIRN002, U24 RR021382
- NIBIB. Grant Numbers: R01 EB001550, R01EB006758, NAMIC U54-EB005149
- NINDS. Grant Numbers: R01 NS052585-01, R01 NS051826
- MIND Institute
- The Autism
- Dyslexia Project
- Ellison Medical Foundation
- hippocampal subfields;
- computational methods;
- statistical modeling
Recent developments in MRI data acquisition technology are starting to yield images that show anatomical features of the hippocampal formation at an unprecedented level of detail, providing the basis for hippocampal subfield measurement. However, a fundamental bottleneck in MRI studies of the hippocampus at the subfield level is that they currently depend on manual segmentation, a laborious process that severely limits the amount of data that can be analyzed. In this article, we present a computational method for segmenting the hippocampal subfields in ultra-high resolution MRI data in a fully automated fashion. Using Bayesian inference, we use a statistical model of image formation around the hippocampal area to obtain automated segmentations. We validate the proposed technique by comparing its segmentations to corresponding manual delineations in ultra-high resolution MRI scans of 10 individuals, and show that automated volume measurements of the larger subfields correlate well with manual volume estimates. Unlike manual segmentations, our automated technique is fully reproducible, and fast enough to enable routine analysis of the hippocampal subfields in large imaging studies. © 2009 Wiley-Liss, Inc.