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

V Sundaresan, G Zamboni, N K Dinsdale, P M Rothwell, L Griffanti and M Jenkinson. " 

Medical Image Analysis, vol. 74, 102215 2021 

Project Page / Paper / Code

Compared various domain adaptation techniques such as transfer learning, domain adversarial neural networks and domain unlearning, to improve the generalisability of our recently proposed triplanar ensemble network.

Multi-Centre, Multi-Vendor and Multi-Disease Cardiac Segmentation: The M&Ms Challenge

V M Campello, P Gkontra, C Izquierdo, C Martín-Isla, A Sojoudi, P M. Full, K Maier-Hein, Y Zhang, Z He, J Ma, M Parreño, A Albiol, F Kong, S C. Shadden, J C Acero, V Sundaresan et al.

IEEE Transactions on Medical Imaging, vol. 40(12), p. 3543-3554, 2021 

Project Page / Paper / Code

Proposed an adversarial domain adaptation-based deep learning method for generalisable cardiac MR segmentation, ranked among top 6 methods presented in the challenge.

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

V Bordin, I Bertani, I Mattioli, V Sundaresan, P McCarthy, S Suri, E Zsoldos, N Filippini, A Mahmood, L Melazzini, and M M Laganà, et al.

NeuroImage, vol. 237, 118189.  2021 

Project Page / Paper / Code

Harmonisation of white matter hyperintensity measures across two major studies of healthy elderly populations, the Whitehall II and the UK Biobank.

A 2-Step Deep Learning Method with Domain Adaptation for Multi-Centre, Multi-Vendor and Multi-Disease Cardiac Magnetic Resonance Segmentation

J A Acero*, V Sundaresan*, N Dinsdale, V Grau, and M Jenkinson

." In International Workshop on Statistical Atlases and Computational Models of the Heart, MICCAI 2020, p. 196-207. Springer, Cham, 2020. 

Project Page / Paper / Code

(* shared first authorship)