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
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
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).