Extraction of Imaging Biomarkers for Neurodegeneration
Automated Detection of Candidate Subjects with Cerebral Microbleeds using Machine Learning
V Sundaresan, C Arthofer, G Zamboni, R A Dineen, P M Rothwell, S N Sotiropoulos, D P Auer, et al.
Frontiers in Neuroinformatics, vol.15, 2022
Detected microbleed candidate subjects from a larger dataset, UK Biobank, using a machine learning-based, computationally light pipeline.
Triplanar ensemble U-Net model for white matter hyperintensities segmentation on MR images
V Sundaresan, G Zamboni, P M Rothwell, M Jenkinson, and L Griffanti
Medical Image Analysis, vol. 73, 102184, 2021
Proposed an ensemble triplanar network that uses anatomical information regarding WMH spatial distribution in loss functions to provide an accurate WMH segmentation.
White matter hyperintensities classified according to intensity and spatial location reveal specific associations with cognitive performance
L Melazzini, C E Mackay, V Bordin, S Suri, E Zsoldos, N Filippini, A Mahmood, V Sundaresan, M Codari, E Duff and A Singh-Manoux et al.
NeuroImage: Clinical, vol.30, 102616. 2021
Project Page / Paper
Developed an automatic method that sub-classifies WMHs using MRI data from the Whitehall II study.
Automated lesion segmentation with BIANCA: impact of population-level features, classification algorithm and locally adaptive thresholding
V Sundaresan, G Zamboni, C Le Heron, P M Rothwell, M Husain, M Battaglini, N De Stefano, M Jenkinson, and L Griffanti
NeuroImage, vol. 202, 116056. 2019
Project Page / Paper / Code
Improve our recently developed FSL tool for white matter hyperintensity segmentation, BIANCA, in order to better deal with the sources of lesion variability.
BIANCA (Brain Intensity AbNormality Classification Algorithm): a new tool for automated segmentation of white matter hyperintensities
L Griffanti, G Zamboni, A Khan, L Li, G Bonifacio, V Sundaresan, U G Schulz et al.
Neuroimage, vol. 141, p. 191-205. 2016
Proposed a fully automated, supervised method for white matter hyperintensity (WMH) detection, was integrated with FSL as the first WMH segmentation tool.
Brain Tumour Segmentation Using a Triplanar Ensemble of U-Nets on MR Images
V Sundaresan, L Griffanti, M Jenkinson
In: Crimi A., Bakas S. (eds) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2020, MICCAI 2020. Lecture Notes in Computer Science, vol 12658. Springer Cham 2021