ArticleMagnetic resonance imaging2019
Registration-based image enhancement improves multi-atlas segmentation of the thalamic nuclei and hippocampal subfields.
Article in Magnetic resonance imaging, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Mapping whole-brain auditory activation with 3T multi-echo fMRI at the group and individual-subject level.Hearing research · 2026Article
- Mapping Whole-Brain Auditory Activation with 3T Multi-Echo fMRI at the Group and Individual-Subject Level.bioRxiv : the preprint server for biology · 2025Article
- Multi-atlas thalamic nuclei segmentation on standard T1-weighed MRI with application to normal aging.Human brain mapping · 2023Article
- Can Autism Be Diagnosed with Artificial Intelligence? A Narrative Review.Diagnostics (Basel, Switzerland) · 2021Review
- In vivo high-resolution structural MRI-based atlas of human thalamic nuclei.Scientific data · 2021Article
- Automated, open-source segmentation of the Hippocampus and amygdala with the open Vanderbilt archive of the temporal lobe.Magnetic resonance imaging · 2021Article
- A Segmentation Method of Foramen Ovale Based on Multiatlas.Computational and mathematical methods in medicine · 2021Article
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Authors and funding
12 authors.
Funding
Abstract
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.
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