member

Biography

Ninon Burgos is a CNRS researcher at the Paris Brain Institute, in the ARAMIS Lab and a fellow of PR[AI]RIE, the PaRis Artificial Intelligence Research InstitutE. She completed her PhD at University College London in the Centre for Medical Image Computing under the supervision of Sébastien Ourselin. She received an MSc in Biomedical Engineering from Imperial College London and an Engineering degree from a French Graduate School in Electrical Engineering and Computer Science (ENSEA). In 2019, she received the ERCIM Cor Baayen Young Researcher Award. Her research focuses on the development of computational imaging tools to improve the understanding and diagnosis of dementia.

Research work

Neuroimaging offers an unmatched description of the brain’s structure and physiology, which explains its crucial role in the understanding, diagnosis, and treatment of neurological disorders. To provide a complete picture of biological processes and their alterations, it is necessary to combine multiple imaging modalities. Using such multimodal data is a difficult task for the clinicians because of the large amount of information available and the difficulty to assess deviations from normal variability. While tremendous progress has been made in the analysis of brain imaging data in the past decade, there is a critical need to develop new data models and analysis tools that can quantitatively process multimodal data and build flexible computer-aided systems to support clinical decisions. Ninon Burgos focuses on the individual analysis of medical images to improve differential diagnosis and strengthen personalised medicine. This involves developing advanced computational representations of multimodal imaging data and building flexible decision support systems that can be applied to brain images to assist in the diagnosis of neurological diseases.

Publications

Burgos, N., Bottani, S., Faouzi, J., Thibeau-Sutre, E., and Colliot, O.: ‘Deep learning in brain disor- ders: from data processing to disease treatment’. Briefings in Bioinformatics, 22(2): 1560–1576, 2021. doi:10.1093/bib/bbaa310 – hal-03070554

Burgos, N., Cardoso, M.J., Samper-González, J., Habert, M.-O., Durrleman, S., Ourselin, S., and Colliot, O.: ‘Anomaly Detection for the Individual Analysis of Brain PET Images’. Journal of Medical Imaging, 8(2): 024003, 2021. doi:10.1117/1.JMI.8.2.024003 – hal-03193306

Wen, J., Thibeau-Sutre, E., Samper-González, J., Routier, A., Bottani, S., Durrleman, S., Burgos, N., and Colliot, O.: ‘Convolutional Neural Networks for Classification of Alzheimer’s Disease: Overview and Reproducible Evaluation’, Medical Image Analysis, 63: 101694, 2020. doi:10.1016/j.media.2020.101694 – hal-02562504

Thibeau-Sutre, E., Colliot, O., Dormont, D., and Burgos, N.: ‘Visualization Approach to Assess the Robustness of Neural Networks for Medical Image Classification’. In SPIE Medical Imaging 2020, 11313: 113131J, 2020. doi:10.1117/12.2548952 – hal-02370532

Burgos, N., Cardoso, M.J., Thielemans, K., Modat, M., Pedemonte, S., Dickson, J., Barnes, A., Ahmed, R., Mahoney, C.J., Schott, J.M., Duncan, J.S., Atkinson, D., Arridge, S.R., Hutton, B.F., and Ourselin, S.: ‘Attenuation Correction Synthesis for Hybrid PET-MR Scanners: Application to Brain Studies’. IEEE Transactions on Medical Imaging, 33(12): 2332–2341, 2014. doi:10.1109/TMI.2014.2340135