Supervised learning aims to build a function or model that seeks as many map**s as
possible between the training data and outputs, where each training data will predict as a …

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 …

Spatially resolved genomic technologies have allowed us to study the physical organization
of cells and tissues, and promise an understanding of local interactions between cells …

The removal of non-brain signal from magnetic resonance imaging (MRI) data, known as
skull-strip**, is an integral component of many neuroimage analysis streams. Despite their …

Image registration is a fundamental medical image analysis task, and a wide variety of
approaches have been proposed. However, only a few studies have comprehensively …

Several autopsy studies recognize the locus coeruleus (LC) as the initial site of
hyperphosphorylated TAU aggregation, and as the number of LC neurons harboring TAU …

We present VoxelMorph, a fast learning-based framework for deformable, pairwise medical
image registration. Traditional registration methods optimize an objective function for each …

Preprocessing of functional magnetic resonance imaging (fMRI) involves numerous steps to
clean and standardize the data before statistical analysis. Generally, researchers create ad …

Deep learning-based methods have recently demonstrated promising results in deformable
image registration for a wide range of medical image analysis tasks. However, existing deep …

Classical deformable registration techniques achieve impressive results and offer a rigorous
theoretical treatment, but are computationally intensive since they solve an optimization …