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Proceedings Paper

Multi-scale hippocampal parcellation improves atlas-based segmentation accuracy
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Paper Abstract

Known for its distinct role in memory, the hippocampus is one of the most studied regions of the brain. Recent advances in magnetic resonance imaging have allowed for high-contrast, reproducible imaging of the hippocampus. Typically, a trained rater takes 45 minutes to manually trace the hippocampus and delineate the anterior from the posterior segment at millimeter resolution. As a result, there has been a significant desire for automated and robust segmentation of the hippocampus. In this work we use a population of 195 atlases based on T1-weighted MR images with the left and right hippocampus delineated into the head and body. We initialize the multi-atlas segmentation to a region directly around each lateralized hippocampus to both speed up and improve the accuracy of registration. This initialization allows for incorporation of nearly 200 atlases, an accomplishment which would typically involve hundreds of hours of computation per target image. The proposed segmentation results in a Dice similiarity coefficient over 0.9 for the full hippocampus. This result outperforms a multi-atlas segmentation using the BrainCOLOR atlases (Dice 0.85) and FreeSurfer (Dice 0.75). Furthermore, the head and body delineation resulted in a Dice coefficient over 0.87 for both structures. The head and body volume measurements also show high reproducibility on the Kirby 21 reproducibility population (R2 greater than 0.95, p < 0.05 for all structures). This work signifies the first result in an ongoing work to develop a robust tool for measurement of the hippocampus and other temporal lobe structures.

Paper Details

Date Published: 24 February 2017
PDF: 7 pages
Proc. SPIE 10133, Medical Imaging 2017: Image Processing, 101332D (24 February 2017); doi: 10.1117/12.2254425
Show Author Affiliations
Andrew J. Plassard, Vanderbilt Univ. (United States)
Maureen McHugo, Vanderbilt Univ. Medical Ctr. (United States)
Stephan Heckers, Vanderbilt Univ. Medical Ctr. (United States)
Bennett A. Landman, Vanderbilt Univ. (United States)

Published in SPIE Proceedings Vol. 10133:
Medical Imaging 2017: Image Processing
Martin A. Styner; Elsa D. Angelini, Editor(s)

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