V
Vaanathi Sundaresan
Researcher at University of Oxford
Publications - 27
Citations - 645
Vaanathi Sundaresan is an academic researcher from University of Oxford. The author has contributed to research in topics: Hyperintensity & Computer science. The author has an hindex of 6, co-authored 24 publications receiving 305 citations. Previous affiliations of Vaanathi Sundaresan include Indian Institutes of Technology & Indian Institute of Technology Madras.
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Journal ArticleDOI
BIANCA (Brain Intensity AbNormality Classification Algorithm): A new tool for automated segmentation of white matter hyperintensities.
Ludovica Griffanti,Giovanna Zamboni,Aamira Khan,Linxin Li,Guendalina Bonifacio,Vaanathi Sundaresan,Ursula G. Schulz,Wilhelm Küker,Marco Battaglini,Peter M. Rothwell,Mark Jenkinson +10 more
TL;DR: The findings suggest that BIANCA, which will be freely available as part of the FSL package, is a reliable method for automated WMH segmentation in large cross-sectional cohort studies.
Journal ArticleDOI
Multi-Centre, Multi-Vendor and Multi-Disease Cardiac Segmentation: The M&Ms Challenge.
Víctor M. Campello,Polyxeni Gkontra,Cristian Izquierdo,Carlos Martín-Isla,Alireza Sojoudi,Peter M. Full,Klaus H. Maier-Hein,Yao Zhang,Zhiqiang He,Jun Ma,Mario Parreño,Alberto Albiol,Fanwei Kong,Shawn C. Shadden,Jorge Corral Acero,Vaanathi Sundaresan,Mina Saber,Mustafa Elattar,Hongwei Li,Bjoern H. Menze,Firas Khader,Christoph Haarburger,Cian M. Scannell,Mitko Veta,Adam Carscadden,Kumaradevan Punithakumar,Xiao Liu,Sotirios A. Tsaftaris,Xiaoqiong Huang,Xin Yang,Lei Li,Xiahai Zhuang,David Vilades,Martín Descalzo,Andrea Guala,Lucia La Mura,Matthias G. Friedrich,Ria Garg,Julie Lebel,Filipe Henriques,Mahir Karakas,Ersin Cavus,Steffen E. Petersen,Sergio Escalera,Santi Seguí,José Rodríguez-Palomares,Karim Lekadir +46 more
TL;DR: The results of the MICCAI 2020 Challenge on generalizable deep learning for cardiac segmentation are presented in this article, where 14 teams submitted different solutions to the problem, combining various baseline models, data augmentation strategies, and domain adaptation techniques.
Proceedings ArticleDOI
Automated characterization of the fetal heart in ultrasound images using fully convolutional neural networks
TL;DR: This work provides a state-of-art solution for detecting the fetal heart and classifying each individual frame as belonging to one of the standard viewing planes using fully convolutional neural networks (FCNs).
Journal ArticleDOI
Automated lesion segmentation with BIANCA: Impact of population-level features, classification algorithm and locally adaptive thresholding
Vaanathi Sundaresan,Giovanna Zamboni,Campbell Le Heron,Peter M. Rothwell,Masud Husain,Marco Battaglini,Nicola De Stefano,Mark Jenkinson,Ludovica Griffanti +8 more
TL;DR: BIANCA was improved and LOCATE, a supervised method for determining optimal local thresholds to apply to the estimated lesion probability map, was proposed, providing a substantial improvement in the lesion segmentation performance, when compared to the global thresholding.
Journal ArticleDOI
Triplanar ensemble U-Net model for white matter hyperintensities segmentation on MR images.
TL;DR: In this paper, an ensemble triplanar network was proposed to combine the predictions from three different planes of brain MR images to provide an accurate WMH segmentation, and the network used anatomical information regarding WMH spatial distribution in loss functions, to improve the efficiency of segmentation and to overcome the contrast variations between deep and periventricular WMHs.