Atlas-based hippocampus segmentation in Alzheimer's disease and mild cognitive impairment

Abstract

This study assesses the performance of public-domain automated methodologies for MRI-based segmentation of the hippocampus in elderly subjects with Alzheimer's disease (AD) and mild cognitive impairment (MCI). Structural MR images of 54 age- and gender-matched healthy elderly individuals, subjects with probable AD, and subjects with MCI were collected at the University of Pittsburgh Alzheimer's Disease Research Center. Hippocampi in subject images were automatically segmented by using AIR, SPM, FLIRT, and the fully deformable method of Chen to align the images to the Harvard atlas, MNI atlas, and randomly selected, manually labeled subject images ("cohort atlases"). Mixed-effects statistical models analyzed the effects of side of the brain, disease state, registration method, choice of atlas, and manual tracing protocol on the spatial overlap between automated segmentations and expert manual segmentations. Registration methods that produced higher degrees of geometric deformation produced automated segmentations with higher agreement with manual segmentations. Side of the brain, presence of AD, choice of reference image, and manual tracing protocol were also significant factors contributing to automated segmentation performance. Fully automated techniques can be competitive with human raters on this difficult segmentation task, but a rigorous statistical analysis shows that a variety of methodological factors must be carefully considered to insure that automated methods perform well in practice. The use of fully deformable registration methods, cohort atlases, and user-defined manual tracings are recommended for highest performance in fully automated hippocampus segmentation.

Automated ventricular mapping with multi-atlas fluid image alignment reveals genetic effects in Alzheimer's disease

“…Recently, we used a single-atlas approach governed by optical flow image registration (rather than the fluid model used here) to segment the hippocampus in aging and AD (Carmichael et al, 2005). Hippocampal segmentation is generally regarded as more challenging than ventricular segmentation.…”

Ventricular volume and dementia progression in the Cardiovascular Health Study

“…Lateral ventricular volumes were estimated fully automatically on all high-resolution scans using a technique described in a previous study and validated on a set of dilated ventricles [5,6]. In short, images were resampled to obtain 1 × 1 × 1 mm 3 voxels, anisotropically smoothed [41], skull-stripped [40], cropped to remove all-zero planes, and nonlinearly aligned [7] to a reference image on which the lateral ventricles had been traced by a trained rater.…”

Multicentre variability of MRI-based medial temporal lobe volumetry in Alzheimer's disease

“…The majority of experimental studies so far employed manual volumetric techniques to determine volumes of hippocampus, amygdala, entorhinal cortex, and other subregions of medial temporal lobe [12,25,50]. Few studies have employed semi-automated techniques to determine hippocampus volume, accounting for more than 80% of variation in manual measurements [7,21]. Manual hippocampus volume reaches about 70% accuracy [12,26] in the prediction of AD in patients with mild cognitive impairment (MCI), an at risk group of AD [32], and between 80 to 90% accuracy in the discrimination between patients with AD and healthy control subjects [17,18].…”