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Dinsdale,N.K.*, Bluemke, E., Sundaresan,V. et al. Challenges for machine learning in clinical translation of big data imaging studies. Neuron (2022). [Preprint / Paper].
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Sundaresan,V.* et al.Automated Detection of Candidate Subjects with Cerebral Microbleeds using Machine Learning. Front. Neuroinform. 15, 80 (2022). [Paper]
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Sundaresan,V.* et al. Constrained self-supervised method with temporal ensembling for fiber bundle detection on anatomic tracing data. Medical Optical Imaging and Virtual Microscopy Image Analysis (eds. Huo, Y., Millis, B. A., Zhou, Y., Wang, X., Harrison, A. P., Xu Z.) Accepted, in press (Springer Nature Switzerland AG, 2022). [Preprint]
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Sundaresan,V.*, Dinsdale,N.K., Jenkinson,M. & Griffanti,L. Omni-supervised domain adversarial training for white matter hyperintensity segmentation in the UK Biobank. International Symposium on Biomedical Imaging, 2022. ISBI 2022. 1-4 IEEE (2022). *Shared first authorship. [Paper]
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Sundaresan,V.*, Jenkinson,M., Zamboni,G. & Griffanti,L. Detection of white matter hyperintensities using Triplanar U-Net ensemble network. International Society for Magnetic Resonance in Medicine, 2020. ISMRM 2020. [Abstract]
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Sundaresan,V.*, Griffanti,L. & Jenkinson,M. Brain Tumour Segmentation Using a Triplanar Ensemble of U-Nets on MR Images. Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries . BrainLes (MICCAI) 2020 (eds. Crimi A., Bakas S.) 340-353 (Springer Cham, 2021). [Preprint]
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AceroJ.A.*, Sundaresan,V., Dinsdale,N., Grau,V. & Jenkinson,M. A 2-Step Deep Learning Method with Domain Adaptation for Multi-Centre, Multi-Vendor and Multi-Disease Cardiac Magnetic Resonance Segmentation." International Workshop on Statistical Atlases and Computational Models of the Heart M&Ms and EMIDEC Challenges, STATCOM (MICCAI) 2020 (eds. Puyol Anton, E., Pop, M., Sermesant, M., Campello, V., Lalande, A., Lekadir, K., Suinesiaputra, A., Camara, O. & Young, A.) 196- 207 (Springer Cham, 2021). *Shared first authorship. [Paper]
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Sundaresan,V.* et al. Comparison of domain adaptation techniques for white matter hyperintensity segmentation in brain MR images. Med Image Anal. 74, 102215 (2021). [Paper]
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Sundaresan,V.*, Zamboni,G., Rothwell,P.M., Jenkinson,M., & Griffanti,L. Triplanar ensemble U-Net model for white matter hyperintensities segmentation on MR images. Med Image Anal. 73, 102184 (2021). [Paper]
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Campello,V.M.*, Gkontra,P., Izquierdo,C., Martín-Isla,C., Sojoudi,A., Full,P.M., Maier-Hein,K., Zhang, Y., He, Z., Ma, J., Parreño, M., Albiol, A., Kong, F., Shadden, S. C., Acero, J, C., Sundaresan, V. et al. Multi-Centre, Multi-Vendor and Multi-Disease Cardiac Segmentation: The M&Ms Challenge. IEEE Trans Med Imaging 40 (12), 3543-3554 (2021). [Paper]
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Bordin,V.*, Bertani,I., Mattioli,I., Sundaresan,V. et al. Integrating large-scale neuroimaging research datasets: harmonisation of white matter hyperintensity measurements across Whitehall and UK Biobank datasets. NeuroImage 237, 118189 (2021). [Paper]
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Melazzini,L.*, Mackay,C.E., Bordin,V., S Suri, Zsoldos,E., Filippini,N., Mahmood,A., Sundaresan, V. et al. White matter hyperintensities classified according to intensity and spatial location reveal specific associations with cognitive performance. NeuroImage Clin. 30, 102616 (2021). [Paper]
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Gentile,G.*, Battaglini,M., Luchetti,L., Giorgio,A., Griffanti,L., Sundaresan,V. et al.BIANCAforan automatic detection of multiple sclerosis lesions using machine learning. Multiple Sclerosis Journal 25, 681 SAGE publications (2019). [paper]
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Sundaresan,V.* et al. Automated lesion segmentation with BIANCA:impact of population-level features, classification algorithm and locally adaptive thresholding. NeuroImage 202, 116056 (2019). [Paper]
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Sundaresan,V.* et al.Modelling the distribution of white matter hyperintensities due to ageing on MRI using Bayesian inference. NeuroImage 185, 434-445 (2019). [Paper]
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Griffanti, L.*, Zamboni, G., Khan, A., Li, L., Bonifacio, G., Sundaresan, V. et al. BIANCA (Brain Intensity AbNormality Classification Algorithm): a new tool for automated segmentation of white matter hyperintensities. NeuroImage 141, 191-205 (2016). [Paper]
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Sundaresan,V.*, Bridge,C.P., Ioannou,C. & Noble,J.A. Automated characterization of the fetal heart in ultrasound images using fully convolutional neural networks. International Symposium on Biomedical Imaging, 2017. ISBI 2017. 1-4 IEEE (2017). [Paper]
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Sundaresan,V.*, Ram,K., Selvaraj,K., Joshi,N. & Sivaprakasam,M. Adaptive super-candidate based approach for detection and classification of drusen on retinal fundus images. OMIA MICCAI (2015). [Paper]
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Sundaresan,V.*, Ram,K., Joshi,N., Sivaprakasam,M. & Gandhi,R. Computer-assisted grading of diabetic macular edema on retinal color fundus images. in Engineering in Medicine and Biology Society, 2015. EMBS 2015. 4330-4333. IEEE (2015). [Paper]
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Sundaresan,V.*, Ram,K., Joshi,N., Sivaprakasam,M & Gandhi,R. Integrated approach for accurate localization of optic disc and macula. in Ophthalmic Medical Image Analysis International Workshop, 2014. OMIA MICCAI (2014). [Paper]