Automated segmentation of hippocampal subfields from ultra-high resolution in vivo MRI.
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.
Van Leemput
Koen
K
Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA 02129, USA. koen@nmr.mgh.harvard.edu
Bakkour
Akram
A
Benner
Thomas
T
Wiggins
Graham
G
Wald
Lawrence L
LL
Augustinack
Jean
J
Dickerson
Bradford C
BC
Golland
Polina
P
Fischl
Bruce
B
D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000375:Aging / Q000473:pathology; D000465:Algorithms; D000544:Alzheimer Disease / Q000473:pathology; D001331:Automation; D001499:Bayes Theorem; D006624:Hippocampus / Q000033:anatomy & histology* / Q000473:pathology; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008875:Middle Aged; D009929:Organ Size; D055815:Young Adult