Brain Morphometry

5 papers with code • 0 benchmarks • 0 datasets

Measurement of brain structures from neuroimaging (MRI).

Most implemented papers

Brain Morphometry Estimation: From Hours to Seconds Using Deep Learning

SCAN-NRAD/BrainRegressorCNN 8 Apr 2020

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

SCAN-NRAD/DL-DiReCT 5 Nov 2020

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

singlaayush/minit 19 Aug 2022

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 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 26 May 2023

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).