Longitudinal Automatic Segmentation of Hippocampal Subfields (LASHiS) using multi-contrast MRI

Thomas Shaw; Ashley York; Maryam Ziaei; Markus Barth; Steffen Bollmann; Alzheimer’s Disease Neuroimaging Initiative

doi:10.1016/j.neuroimage.2020.116798

Longitudinal Automatic Segmentation of Hippocampal Subfields (LASHiS) using multi-contrast MRI

Neuroimage. 2020 Sep:218:116798. doi: 10.1016/j.neuroimage.2020.116798. Epub 2020 Apr 18.

Authors

Thomas Shaw¹, Ashley York², Maryam Ziaei³, Markus Barth⁴, Steffen Bollmann⁵; Alzheimer’s Disease Neuroimaging Initiative

Affiliations

¹ Centre for Advanced Imaging, The University of Queensland, Brisbane, Australia. Electronic address: t.shaw@uq.edu.au.
² Queensland Brain Institute, The University of Queensland, Brisbane, Australia.
³ Centre for Advanced Imaging, The University of Queensland, Brisbane, Australia; Queensland Brain Institute, The University of Queensland, Brisbane, Australia.
⁴ Centre for Advanced Imaging, The University of Queensland, Brisbane, Australia; School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Australia; ARC Training Centre for Innovation in Biomedical Imaging Technology, The University of Queensland, Brisbane, QLD, Australia.
⁵ Centre for Advanced Imaging, The University of Queensland, Brisbane, Australia; ARC Training Centre for Innovation in Biomedical Imaging Technology, The University of Queensland, Brisbane, QLD, Australia.

PMID: 32311467
DOI: 10.1016/j.neuroimage.2020.116798

Abstract

The volumetric and morphometric examination of hippocampus formation subfields in a longitudinal manner using in vivo MRI could lead to more sensitive biomarkers for neuropsychiatric disorders and diseases including Alzheimer's disease, as the anatomical subregions are functionally specialised. Longitudinal processing allows for increased sensitivity due to reduced confounds of inter-subject variability and higher effect-sensitivity than cross-sectional designs. We examined the performance of a new longitudinal pipeline (Longitudinal Automatic Segmentation of Hippocampus Subfields [LASHiS]) against three freely available, published approaches. LASHiS automatically segments hippocampus formation subfields by propagating labels from cross-sectionally labelled time point scans using joint-label fusion to a non-linearly realigned 'single subject template', where image segmentation occurs free of bias to any individual time point. Our pipeline measures tissue characteristics available in in vivo high-resolution MRI scans, at both clinical (3 T) and ultra-high field strength (7 T) and differs from previous longitudinal segmentation pipelines in that it leverages multi-contrast information in the segmentation process. LASHiS produces robust and reliable automatic multi-contrast segmentations of hippocampus formation subfields, as measured by higher volume similarity coefficients and Dice coefficients for test-retest reliability and robust longitudinal Bayesian Linear Mixed Effects results at 7 T, while showing sound results at 3 T. All code for this project including the automatic pipeline is available at https://github.com/CAIsr/LASHiS.

Keywords: Computer-assisted; Hippocampus; Image processing; Longitudinal studies; Magnetic resonance imaging; Segmentation.

Publication types

Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

Adult
Female
Hippocampus / anatomy & histology*
Humans
Image Processing, Computer-Assisted / methods*
Magnetic Resonance Imaging / methods
Male
Neuroimaging / methods*
Software

Abstract

Publication types

MeSH terms

Grants and funding