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Found 8 papers, 3 papers with code
Multiscale cortical morphometry reveals pronounced regional and scale-dependent variations across the lifespan
Motivation: Characterising the changes in cortical morphology across the lifespan is fundamental for a range of research and clinical applications.
CortexMorph: fast cortical thickness estimation via diffeomorphic registration using VoxelMorph
Recent studies using a synthetic cortical thickness phantom have demonstrated that the combination of DiReCT and deep-learning-based segmentation is more sensitive to subvoxel cortical thinning than Freesurfer.
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.
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.
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.
Automatic detection of lesion load change in Multiple Sclerosis using convolutional neural networks with segmentation confidence
Instead, we propose a method for identifying lesion changes of high certainty, and establish on a dataset of longitudinal multiple sclerosis cases that this method is able to separate progressive from stable timepoints with a very high level of discrimination (AUC = 0. 99), while changes in lesion volume are much less able to perform this separation (AUC = 0. 71).
Few-shot brain segmentation from weakly labeled data with deep heteroscedastic multi-task networks
In applications of supervised learning applied to medical image segmentation, the need for large amounts of labeled data typically goes unquestioned.
Simultaneous lesion and neuroanatomy segmentation in Multiple Sclerosis using deep neural networks
We trained two state-of-the-art fully convolutional CNN architectures on the 2016 MSSEG training dataset, which was annotated by seven independent human raters: a reference implementation of a 3D Unet, and a more recently proposed 3D-to-2D architecture (DeepSCAN).
