Automated algorithm to measure changes in medial temporal lobe volume in Alzheimer disease.

Abstract Background The change in volume of anatomic structures is as a sensitive indicator of Alzheimer disease (AD) progression. Although several methods are available to measure brain volumes, improvements in speed and automation are required. Our objective was to develop a fully automated, fast, and reliable approach to measure change in medial temporal lobe (MTL) volume, including primarily hippocampus. Methods The MTL volume defined in an atlas image was propagated onto each baseline image and a level set algorithm was applied to refine the shape and smooth the boundary. The MTL of the baseline image was then mapped onto the corresponding follow-up image to measure volume change (ΔMTL). Baseline and 24 months 3D T 1 -weighted images from the Alzheimer Disease Neuroimaging Initiative (ADNI) were randomly selected for 50 normal elderly controls (NECs), 50 subjects with mild cognitive impairment (MCI) and 50 subjects with AD to test the algorithm. The method was compared to the FreeSurfer segmentation tools. Results The average ΔMTL (mean ± SEM) was 68 ± 35 mm 3 in NEC, 187 ± 38 mm 3 in MCI and 300 ± 34 mm 3 in the AD group and was significantly different ( p Comparison with existing method(s) Results for the FreeSurfer software were similar but did not detect significant differences between the MCI and AD groups. Conclusion This novel segmentation approach is fully automated and provides a robust marker of brain atrophy that shows different rates of atrophy over 2 years between NEC, MCI, and AD groups.