Deep learning for brain MRI segmentation: state of the art and future directions

Z Akkus, A Galimzianova, A Hoogi, DL Rubin… - Journal of digital …, 2017 - Springer
Quantitative analysis of brain MRI is routine for many neurological diseases and conditions
and relies on accurate segmentation of structures of interest. Deep learning-based …

Deformable medical image registration: A survey

A Sotiras, C Davatzikos… - IEEE transactions on …, 2013 - ieeexplore.ieee.org
Deformable image registration is a fundamental task in medical image processing. Among
its most important applications, one may cite: 1) multi-modality fusion, where information …

fMRIPrep: a robust preprocessing pipeline for functional MRI

O Esteban, CJ Markiewicz, RW Blair, CA Moodie… - Nature …, 2019 - nature.com
Preprocessing of functional magnetic resonance imaging (fMRI) involves numerous steps to
clean and standardize the data before statistical analysis. Generally, researchers create ad …

Voxelmorph: a learning framework for deformable medical image registration

G Balakrishnan, A Zhao, MR Sabuncu… - IEEE transactions on …, 2019 - ieeexplore.ieee.org
We present VoxelMorph, a fast learning-based framework for deformable, pairwise medical
image registration. Traditional registration methods optimize an objective function for each …

The minimal preprocessing pipelines for the Human Connectome Project

MF Glasser, SN Sotiropoulos, JA Wilson, TS Coalson… - Neuroimage, 2013 - Elsevier
Abstract The Human Connectome Project (HCP) faces the challenging task of bringing
multiple magnetic resonance imaging (MRI) modalities together in a common automated …

A reproducible evaluation of ANTs similarity metric performance in brain image registration

BB Avants, NJ Tustison, G Song, PA Cook, A Klein… - Neuroimage, 2011 - Elsevier
The United States National Institutes of Health (NIH) commit significant support to open-
source data and software resources in order to foment reproducibility in the biomedical …

Harmonization of cortical thickness measurements across scanners and sites

JP Fortin, N Cullen, YI Sheline, WD Taylor, I Aselcioglu… - Neuroimage, 2018 - Elsevier
With the proliferation of multi-site neuroimaging studies, there is a greater need for handling
non-biological variance introduced by differences in MRI scanners and acquisition …

elastix: A Toolbox for Intensity-Based Medical Image Registration

S Klein, M Staring, K Murphy… - IEEE transactions on …, 2009 - ieeexplore.ieee.org
Medical image registration is an important task in medical image processing. It refers to the
process of aligning data sets, possibly from different modalities (eg, magnetic resonance …

An unsupervised learning model for deformable medical image registration

G Balakrishnan, A Zhao, MR Sabuncu… - Proceedings of the …, 2018 - openaccess.thecvf.com
We present a fast learning-based algorithm for deformable, pairwise 3D medical image
registration. Current registration methods optimize an objective function independently for …

Unsupervised learning of probabilistic diffeomorphic registration for images and surfaces

AV Dalca, G Balakrishnan, J Guttag, MR Sabuncu - Medical image analysis, 2019 - Elsevier
Classical deformable registration techniques achieve impressive results and offer a rigorous
theoretical treatment, but are computationally intensive since they solve an optimization …