Direct cortical thickness estimation using deep learning-based anatomy segmentation and cortex parcellation.

Rebsamen, Michael; Rummel, Christian; Reyes, Mauricio; Wiest, Roland; McKinley, Richard (2020). Direct cortical thickness estimation using deep learning-based anatomy segmentation and cortex parcellation. (In Press). Human brain mapping Wiley-Blackwell 10.1002/hbm.25159

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Accurate and reliable measures of cortical thickness from magnetic resonance imaging are an important biomarker to study neurodegenerative and neurological disorders. Diffeomorphic registration-based cortical thickness (DiReCT) is a known technique to derive such measures from non-surface-based volumetric tissue maps. ANTs provides an open-source method for estimating cortical thickness, derived by applying DiReCT to an atlas-based segmentation. In this paper, we propose DL+DiReCT, a method using high-quality deep learning-based neuroanatomy segmentations followed by DiReCT, yielding accurate and reliable cortical thickness measures in a short time. We evaluate the methods on two independent datasets and compare the results against surface-based measures from FreeSurfer. Good correlation of DL+DiReCT with FreeSurfer was observed (r = .887) for global mean cortical thickness compared to ANTs versus FreeSurfer (r = .608). Experiments suggest that both DiReCT-based methods had higher sensitivity to changes in cortical thickness than Freesurfer. However, while ANTs showed low scan-rescan robustness, DL+DiReCT showed similar robustness to Freesurfer. Effect-sizes for group-wise differences of healthy controls compared to individuals with dementia were highest with the deep learning-based segmentation. DL+DiReCT is a promising combination of a deep learning-based method with a traditional registration technique to detect subtle changes in cortical thickness.

Item Type:

Journal Article (Original Article)

Division/Institute:

04 Faculty of Medicine > Department of Radiology, Neuroradiology and Nuclear Medicine (DRNN) > Institute of Diagnostic and Interventional Neuroradiology
10 Strategic Research Centers > ARTORG Center for Biomedical Engineering Research

Graduate School:

Graduate School for Cellular and Biomedical Sciences (GCB)

UniBE Contributor:

Rebsamen, Michael Andreas; Rummel, Christian; Reyes, Mauricio; Wiest, Roland and McKinley, Richard Iain

Subjects:

500 Science > 570 Life sciences; biology
600 Technology > 610 Medicine & health

ISSN:

1065-9471

Publisher:

Wiley-Blackwell

Funders:

[42] Schweizerischer Nationalfonds

Language:

English

Submitter:

Martin Zbinden

Date Deposited:

27 Aug 2020 15:22

Last Modified:

27 Aug 2020 15:22

Publisher DOI:

10.1002/hbm.25159

PubMed ID:

32786059

Uncontrolled Keywords:

MRI brain morphometry cortical thickness deep learning diffeomorphic registration gray matter atrophy neuroanatomy segmentation

URI:

https://boris.unibe.ch/id/eprint/146141

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