2007 | OriginalPaper | Buchkapitel
Fully Automatic Segmentation of the Hippocampus and the Amygdala from MRI Using Hybrid Prior Knowledge
verfasst von : Marie Chupin, Alexander Hammers, Eric Bardinet, Olivier Colliot, Rebecca S. N. Liu, John S. Duncan, Line Garnero, Louis Lemieux
Erschienen in: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2007
Verlag: Springer Berlin Heidelberg
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The segmentation of macroscopically ill-defined and highly variable structures, such as the hippocampus
Hc
and the amygdala
Am
, from MRI requires specific constraints. Here, we describe and evaluate a hybrid segmentation method that uses knowledge derived from a probabilistic atlas and from anatomical landmarks based on stable anatomical characteristics of the structures. Combined in a previously published semi-automatic segmentation method, they lead to a fast, robust and accurate fully automatic segmentation of
Hc
and
Am
. The probabilistic atlas was built from 16 young controls and registered with the ”unified segmentation” of SPM5. The algorithm was quantitatively evaluated with respect to manual segmentation on two MRI datasets: the 16 young controls, with a leave-one-out strategy, and a mixed cohort of 8 controls and 15 subjects with epilepsy with variable hippocampal sclerosis. The segmentation driven by hybrid knowledge leads to greatly improved results compared to that obtained by registration of the thresholded atlas alone: mean overlap for
Hc
on the 16 young controls increased from 78% to 87% (
p
< 0.001) and on the mixed cohort from 73% to 82% (
p
< 0.001) while the error on volumes decreased from 10% to 7% (
p
< 0.005) and from 18% to 8% (
p
< 0.001), respectively. Automatic results were better than the semi-automatic results: for the 16 young controls, average overlap increased from 84% to 87% (
p
< 0.001) for
Hc
and from 81% to 84% (
p
< 0.002) for
Am
, with equivalent improvements in volume error.