Automated Segmentation of Hippocampal Subfields From Ultra-High Resolution In Vivo MRI
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Golland_Automated Segmentation.pdf
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Author(s)
Van Leemput, Koen
Bakkour, Akram
Benner, Thomas
Wiggins, Graham
Wald, Lawrence
Augustinack, Jean
Dickerson, Bradford C.
Golland, Polina
Fischl, Bruce
Date Issued
May 2009
Journal
Hippocampus
Publisher
Wiley-Blackwell Pubishers
Citation
Van Leemput, Koen et al. “Automated Segmentation of Hippocampal Subfields from Ultra-high Resolution in Vivo MRI.” Hippocampus 19.6 (2009): 549–557.
Version
Author's final manuscript
Abstract
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.
MIT Department
Harvard University--MIT Division of Health Sciences and Technology
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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Creative Commons Attribution-Noncommercial-Share Alike 3.0
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DOI of Published Version
http://dx.doi.org/ 10.1002/hipo.20615
https://doi.org/10.1002/hipo.20615
https://doi.org/10.1002/hipo.20615
