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. 2016 Oct 4;54(3):1027-1037.
doi: 10.3233/JAD-160014.

Clinical Application of Automatic Segmentation of Medial Temporal Lobe Subregions in Prodromal and Dementia-Level Alzheimer's Disease

Eske Christiane Gertje  1   2   3 , John Pluta  3 , Sandhitsu Das  3   4 , Lauren Mancuso  4 , Dasha Kliot  4 , Paul Yushkevich  3 , David Wolk  4
Affiliations

Clinical Application of Automatic Segmentation of Medial Temporal Lobe Subregions in Prodromal and Dementia-Level Alzheimer's Disease

Eske Christiane Gertje et al. J Alzheimers Dis. .

Abstract

Background: Volumetry of medial temporal lobe (MTL) structures to diagnose Alzheimer's disease (AD) in its earliest symptomatic stage could be of great importance for interventions or disease modifying pharmacotherapy.

Objective: This study aimed to demonstrate the first application of an automatic segmentation method of MTL subregions in a clinical population. Automatic segmentation of magnetic resonance images (MRIs) in a research population has previously been shown to detect evidence of neurodegeneration in MTL subregions and to help discriminate AD and mild cognitive impairment (MCI) from a healthy comparison group.

Methods: Clinical patients were selected and T2-weighted MRI scan quality was checked. An automatic segmentation method of hippocampal subfields (ASHS) was applied to scans of 67 AD patients, 38 amnestic MCI patients, and 57 healthy controls. Hippocampal subfields, entorhinal cortex (ERC), and perirhinal cortex were automatically labeled and subregion volumes were compared between groups.

Results: One fourth of all scans were excluded due to bad scan quality. There were significant volume reductions in all subregions, except BA36, in aMCIs (p < 0.001), most prominently in Cornu Ammonis 1 (CA1) and ERC, and in all subregions in AD. However, sensitivity of CA1 and ERC hardly differed from sensitivity of WH in aMCI and AD.

Conclusion: Applying automatic segmentation of MTL subregions in a clinical setting as a potential biomarker for prodromal AD is feasible, but issues of image quality due to motion remain to be addressed. CA1 and ERC provided strongest group discrimination in differentiating aMCIs from controls, but discriminatory power of different subfields was low overall.

Keywords: Alzheimer’s disease; Cornu Ammonis; anatomy; biomarker; diagnosis; entorhinal cortex; hippocampal subfields; hippocampus; histology; magnetic resonance imaging; medial temporal lobe; mild cognitive impairment.

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Figures

Fig. 1
Fig. 1
Illustration of automatic segmentation of the right medial temporal lobe subfields in a coronal T2-weighted MRI scan. CA1, Cornu Ammonis 1; CA2, Cornu Ammonis 2; CA3, Cornu Ammonis 3; DG, dentate gyrus; SUB, subiculum; ERC, entorhinal cortex; BA35, Brodmann area 35; BA36, Brodmann area 36.
Fig. 2
Fig. 2
Group comparisons for all subfield volumes. HC, healthy control; aMCI, amnestic mild cognitive impairment; AD, Alzheimer’s disease; CA1, Cornu Ammonis 1; DG, dentate gyrus; SUB, subiculum; ERC, entorhinal cortex; BA35, Brodman area 35; BA36, Brodman area 36; WH, whole hippocampus.
Fig. 3
Fig. 3
Receiver operating characteristic (ROC) curves and area under the curve (AUC) values for MTL subfields in aMCI versus HC, and AD versus HC. AUC = 0.5 (discrimination no better than chance), AUC = 1 (perfect discrimination); *normalized MTL structures.
See this image and copyright information in PMC

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References

    1. Snyder PJ, Kahle-Wrobleski K, Brannan S, Miller DS, Schindler RJ, DeSanti S, Ryan JM, Morrison G, Grundman M, Chandler J, Caselli RJ, Isaac M, Bain L, Carrillo MC. Assessing cognition and function in Alzheimer’s disease clinical trials: Do we have the right tools? Alzheimers Dement. 2014;10:853–860. - PubMed
    1. Tucker AM, Stern Y. Cognitive reserve in aging. Curr Alzheimer Res. 2011;8:354–360. - PMC - PubMed
    1. Ossenkoppele R, Cohn-Sheehy BI, La Joie R, Vogel JW, Moller C, Lehmann M, van Berckel BN, Seeley WW, Pijnenburg YA, Gorno-Tempini ML, Kramer JH, Barkhof F, Rosen HJ, van der Flier WM, Jagust WJ, Miller BL, Scheltens P, Rabinovici GD. Atrophy patterns in early clinical stages across distinct phenotypes of Alzheimer’s disease. Hum Brain Mapp. 2015;36:4421–4437. - PMC - PubMed
    1. Albert MS, DeKosky ST, Dickson D, Dubois B, Feldman HH, Fox NC, Gamst A, Holtzman DM, Jagust WJ, Petersen RC, Snyder PJ, Carrillo MC, Thies B, Phelps CH. The diagnosis of mild cognitive impairment due to Alzheimer’s disease: Recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement. 2011;7:270–279. - PMC - PubMed
    1. McKhann GM, Knopman DS, Chertkow H, Hyman BT, Jack CR, Jr, Kawas CH, Klunk WE, Koroshetz WJ, Manly JJ, Mayeux R, Mohs RC, Morris JC, Rossor MN, Scheltens P, Carrillo MC, Thies B, Weintraub S, Phelps CH. The diagnosis of dementia due to Alzheimer’s disease: Recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement. 2011;7:263–269. - PMC - PubMed

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