Vaanathi Sundaresan

Self-supervised segmentation and characterization of fiber bundles in anatomic tracing data

This paper introduces a deep-learning method for segmenting fiber bundles in histological tracing data, achieving 80% true positive rate with limited manual labels.

Comparison of domain adaptation techniques for white matter hyperintensity segmentation in brain MR images

This study compares domain adaptation techniques to improve white matter hyperintensity segmentation across different MRI datasets, finding that unsupervised domain adversarial training performs best.

Triplanar ensemble U-Net model for white matter hyperintensities segmentation on MR images

This paper proposes a triplanar ensemble U-Net model for WMH segmentation on MR images, achieving robust performance comparable to existing methods.

Integrating large-scale neuroimaging research datasets: harmonisation of white matter hyperintensity measurements across Whitehall and UK Biobank datasets

This paper harmonises white matter hyperintensity measurements across two neuroimaging datasets by standardising preprocessing and modelling demographic and physiological differences.

Automated lesion segmentation with BIANCA: impact of population-level features, classification algorithm and locally adaptive thresholding

This paper improves the BIANCA tool for WMH segmentation by introducing a locally adaptive thresholding method, LOCATE, which significantly enhances segmentation performance compared to global thresholding.

Modelling the distribution of white matter hyperintensities due to ageing on MRI images using Bayesian inference

This paper develops a Bayesian spline method to model the spatio-temporal distribution of white matter hyperintensities with respect to age and validates it on simulated and real datasets.