I obtained my Bachelor’s degree in Electronic and Communication Engineering, and Master’s degree in Ophthalmology, both in the University of Hong Kong. During that time, I started to be fascinated by the prospective future of using medical imaging methods to investigate and tackle neuroscience problems. I then joined the DTP in 2011, and my research project involves joint collaboration between two centres in UCL: Centre of Medical Image Computing and Centre of Advanced Biomedical Imaging. My research project is to develop automatic segmentation method on mouse brain images and to identify fine structural differences or changes. The project is also part of the mouse phenotyping group project, which is to use high-resolution microscopic MRI to investigate microstructural changes in the mouse brain with the corresponding genomic variations or defects.
Preclinical brain analysis, Image segmentation
My current research is focusing on buiding the translational framework of multi-atlas-based automatic brain structural parcellation for mouse brain MRI. The framework has been made open-source for public access through Github. The framework detail is described in the website here.
D. Ma, M.J. Cardoso, M.A. Zuluaga, M Modat, N Powell, F Wiseman, V Tybulewicz, E Fisher, MF Lythgoe, S. Ourselin: Grey matter sublayer thickness estimation in the mouse cerebellum. Medical Image Computing and Computer-Assisted Intervention. MICCAI 2015
D. Ma, M.J. Cardoso, M. Modat, N. Powell, J. Wells, H. Holmes, F. Wiseman, V. Tybulewicz, E. Fisher, M.F. Lythgoe, S. Ourselin: Automatic structural parcellation of Mouse Brain MRI using Multi Atlas label fusion. PLOS ONE, 2014
D. Ma, M.J. Cardoso, M.F. Lythgoe, S. Ourselin: Cortical thickness map: an automatic quantification of cerebral cortex for in vivo mouse brain MRI. British Chapter ISMRM, 2013
D. Ma, M.J. Cardoso, M. Modat, N. Powell, H. Holmes, M.F. Lythgoe, and S. Ourselin:Multi-atlas segmentation applied to in vivo mouse brain MRI. MICCAI 2012 Workshop on Multi-Atlas Labeling