Registration-based image enhancement improves multi-atlas segmentation of the thalamic nuclei and hippocampal subfields.
Big data
Deep brain structure
Non-rigid registration
Journal
Magnetic resonance imaging
ISSN: 1873-5894
Titre abrégé: Magn Reson Imaging
Pays: Netherlands
ID NLM: 8214883
Informations de publication
Date de publication:
06 2019
06 2019
Historique:
received:
24
08
2018
revised:
09
03
2019
accepted:
13
03
2019
pubmed:
19
3
2019
medline:
5
9
2019
entrez:
19
3
2019
Statut:
ppublish
Résumé
Magnetic resonance imaging (MRI) is an important tool for analysis of deep brain grey matter structures. However, analysis of these structures is limited due to low intensity contrast typically found in whole brain imaging protocols. Herein, we propose a big data registration-enhancement (BDRE) technique to augment the contrast of deep brain structures using an efficient large-scale non-rigid registration strategy. Direct validation is problematic given a lack of ground truth data. Rather, we validate the usefulness and impact of BDRE for multi-atlas (MA) segmentation on two sets of structures of clinical interest: the thalamic nuclei and hippocampal subfields. The experimental design compares algorithms using T1-weighted 3 T MRI for both structures (and additional 7 T MRI for the thalamic nuclei) with an algorithm using BDRE. As baseline comparisons, a recent denoising (DN) technique and a super-resolution (SR) method are used to preprocess the original 3 T MRI. The performance of each MA segmentation is evaluated by the Dice similarity coefficient (DSC). BDRE significantly improves mean segmentation accuracy over all methods tested for both thalamic nuclei (3 T imaging: 9.1%; 7 T imaging: 15.6%; DN: 6.9%; SR: 16.2%) and hippocampal subfields (3 T T1 only: 8.7%; DN: 8.4%; SR: 8.6%). We also present DSC performance for each thalamic nucleus and hippocampal subfield and show that BDRE can help MA segmentation for individual thalamic nuclei and hippocampal subfields. This work will enable large-scale analysis of clinically relevant deep brain structures from commonly acquired T1 images.
Identifiants
pubmed: 30880111
pii: S0730-725X(18)30404-1
doi: 10.1016/j.mri.2019.03.014
pmc: PMC6747695
mid: NIHMS1526020
pii:
doi:
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, N.I.H., Intramural
Research Support, U.S. Gov't, Non-P.H.S.
Langues
eng
Sous-ensembles de citation
IM
Pagination
143-152Subventions
Organisme : NCATS NIH HHS
ID : UL1 TR000445
Pays : United States
Organisme : NIA NIH HHS
ID : P30 AG066507
Pays : United States
Organisme : NEI NIH HHS
ID : R21 EY024036
Pays : United States
Organisme : Intramural NIH HHS
ID : ZIA AG000191-22
Pays : United States
Organisme : NCRR NIH HHS
ID : UL1 RR024975
Pays : United States
Organisme : NINDS NIH HHS
ID : R01 NS095291
Pays : United States
Organisme : Intramural NIH HHS
ID : ZIA AG000191-21
Pays : United States
Organisme : NINDS NIH HHS
ID : R21 NS064534
Pays : United States
Informations de copyright
Copyright © 2019 Elsevier Inc. All rights reserved.
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