Dr. Vaanathi Sundaresan
Colleges
Curriculum Vitae
Vaanathi_sundaresan_CV.pdf
- Vaanathi Sundaresan_CV_2022.pdf
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Vaanathi Sundaresan
Honorary Research Fellow
- Assistant Professor, Department of Computational and Data Sciences (CDS), Indian Institute of Science (IISc), Bangalore, India.
Biography
About me
My work with Prof. Mark Jenkinson and Dr. Ludovica Griffanti, at Oxford centre for Functional MRI of the brain (FMRIB), Wellcome Centre for Integrative Neuroimaging (WIN), involves development of deep learning-based tools for domain adaption of segmentation methods and harmonisation of data from various sources.
My DPhil work was on AI-driven automated detection of small vessel disease signs on brain MR images. My postdoctoral research focusses on domain adaptation and harmonisation of neuroimaging data using deep learning methods.
Research
My research interests include domain adaptation of segmentation models for segmentation of dementia signs and harmonisation of structural data as preprocessing step for further robust analyses. My DPhil work was on modelling the distribution of white matter lesions within a population and the detection of white matter pathologies of the brain in MR images.
Key publications
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Triplanar ensemble U-Net model for white matter hyperintensities segmentation on MR images
Journal article
Sundaresan V. et al, (2020)
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Modelling the distribution of white matter hyperintensities due to ageing on MRI images using Bayesian inference.
Journal article
Sundaresan V. et al, (2019), NeuroImage, 185, 434 - 445
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Automated lesion segmentation with BIANCA: impact of population-level features, classification algorithm and locally adaptive thresholding
Journal article
Sundaresan V. et al, (2018)
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Brain tumour segmentation using a triplanar ensemble of U-Nets
Chapter
Sundaresan V. et al, (2021)
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BIANCA (Brain Intensity AbNormality Classification Algorithm): A new tool for automated segmentation of white matter hyperintensities.
Journal article
Griffanti L. et al, (2016), Neuroimage, 141, 191 - 205
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Automated characterization of the fetal heart in ultrasound images using fully convolutional neural networks
Conference paper
Sundaresan V. et al, (2017), Proceedings - International Symposium on Biomedical Imaging, 671 - 674
Recent publications
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Challenges for machine learning in clinical translation of big data imaging studies.
Journal article
Dinsdale NK. et al, (2022), Neuron, 110, 3866 - 3881
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Multi-Centre, Multi-Vendor and Multi-Disease Cardiac Segmentation: The M&Ms Challenge.
Journal article
Campello VM. et al, (2021), IEEE Trans Med Imaging, 40, 3543 - 3554