Brain Morphometry
5 papers with code • 0 benchmarks • 0 datasets
Measurement of brain structures from neuroimaging (MRI).
Benchmarks
These leaderboards are used to track progress in Brain Morphometry
Most implemented papers
Brain Morphometry Estimation: From Hours to Seconds Using Deep Learning
We propose a deep learning-based approach to predict the volumes of anatomically delineated subcortical regions of interest (ROI), and mean thicknesses and curvatures of cortical parcellations directly from T1-weighted MRI.
Direct cortical thickness estimation using deep learning‐based anatomy segmentation and cortex parcellation
DL+DiReCT is a promising combination of a deep learning‐based method with a traditional registration technique to detect subtle changes in cortical thickness.
Multiple Instance Neuroimage Transformer
As a proof-of-concept, we evaluate the efficacy of our model by training it to identify sex from T1w-MRIs of two public datasets: Adolescent Brain Cognitive Development (ABCD) and the National Consortium on Alcohol and Neurodevelopment in Adolescence (NCANDA).
Reliable brain morphometry from contrast‐enhanced T1w‐MRI in patients with multiple sclerosis
SCAN-NRAD/DL-DiReCT
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Human Brain Mapping 2022
The segmentations were derived with FreeSurfer from the non-enhanced image and used as ground truth for the coregistered CE image.
Fast refacing of MR images with a generative neural network lowers re-identification risk and preserves volumetric consistency
acit-lausanne/refacing-cgan
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To evaluate the performance of the proposed de-identification tool, a comparative study was conducted between several existing defacing and refacing tools, with two different segmentation algorithms (FAST and Morphobox).
