Automated volumetry and regional thickness analysis of hippocampal subfields and medial temporal cortical structures in mild cognitive impairment

Abstract

We evaluate a fully automatic technique for labeling hippocampal subfields and cortical subregions in the medial temporal lobe (MTL) in in vivo 3 Tesla MRI. The method performs segmentation on a T2-weighted MRI scan with 0.4 × 0.4 × 2.0 mm3 resolution, partial brain coverage, and oblique orientation. Hippocampal subfields, entorhinal cortex, and perirhinal cortex are labeled using a pipeline that combines multi-atlas label fusion and learning-based error correction. In contrast to earlier work on automatic subfield segmentation in T2-weighted MRI (Yushkevich et al., 2010), our approach requires no manual initialization, labels hippocampal subfields over a greater anterior-posterior extent, and labels the perirhinal cortex, which is further subdivided into Brodmann areas 35 and 36. The accuracy of the automatic segmentation relative to manual segmentation is measured using cross-validation in 29 subjects from a study of amnestic Mild Cognitive Impairment (aMCI), and is highest for the dentate gyrus (Dice coefficient is 0.823), CA1 (0.803), perirhinal cortex (0.797) and entorhinal cortex (0.786) labels. A larger cohort of 83 subjects is used to examine the effects of aMCI in the hippocampal region using both subfield volume and regional subfield thickness maps. Most significant differences between aMCI and healthy aging are observed bilaterally in the CA1 subfield and in the left Brodmann area 35. Thickness analysis results are consistent with volumetry, but provide additional regional specificity and suggest non-uniformity in the effects of aMCI on hippocampal subfields and MTL cortical subregions.

P2‐231: Systematic Comparison of Different Techniques to Measure Hippocampal Subfield Volumes in ADNI2

“…Validation: This method has been validated using k-fold cross-validation against manual segmentation in an in-vivo dataset of older healthy controls and aMCI patients (Yushkevich et al, 2015b).…”

“…These techniques were applied to a variety of diseases in small single-site studies and usually found subfield volumetry to be superior to standard whole hippocampal volumetry for the detection of hippocampal damage in the early stages of the disease process, for differentiating between diseases or for the investigation of structure/function relationships (e.g., Wang et al, 2006;Ballmaier et al, 2008;Mueller et al, 2008;Schobel et al, 2009;Neylan et al, 2010;Bender et al, 2013;Kerchner et al, 2014;SchoeneBake et al, 2014;Chao et al, 2014;Hsu et al, 2015;De Flores et al, 2015;Pluta et al, 2012;Yushkevich et al, 2015b). The promise of hippocampal subfield volumetry techniques however led to two unexpected developments that could potentially limit the usefulness of this approach.…”

“…This has been exploited by manual labeling approaches and increasingly also by automated approaches. Of the currently available approaches ASHS (Yushkevich et al, 2015b) and the latest version of Bayesian inference labeling implemented in Freesurfer 6.0 (Iglesias et al, 2015) were chosen for this project because both algorithms are publicly available and are used by the larger research community. The manual labeling protocol developed by Mueller et al (2007) was chosen as manual reference because its limited labeling scheme made it possible to label the over 100 images required for this project within a reasonable amount of time.…”

Seven-Tesla MRI and neuroimaging biomarkers for Alzheimer’s disease

“…50,98 Nevertheless, this presents a more complex problem for functional and diffusion MRI as compared with the structural imaging we discuss here. Finally, in addition to imaging improvements, image analysis techniques (such as automated hippocampal segmentation 101 ) need to be developed to capture relatively smaller subfields or pathways, 70 such as the endfolial pathway, in atrophied hippocampi of elderly, mildly cognitively impaired, and AD patients.…”

Will PET Amyloid Imaging Lead to Overdiagnosis of Alzheimer Dementia?

“…There have been many efforts to use computer analysis to expand the diagnostic and prognostic data from MRI studies (30,31); however, these require nonroutine imaging sequences and advanced postprocessing not generally available outside of research studies. Furthermore, although many are proven effective at the sites where they are developed, dissemination of these advanced analytical techniques between institutions has proven challenging (32)(33)(34)(35). Like CSF measurements, sensitivity and specificity of hippocampal volume calculations have shown limited ability to predict who will convert from MCI to AD and are often combined with other biomarkers (36)(37)(38)(39)(40).…”