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About me

I am 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. I completed my PhD at University College London in the Centre for Medical Image Computing under the supervision of Sébastien Ourselin. I 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, I received the ERCIM Cor Baayen Young Researcher Award.

My research currently focuses on the development of computational imaging tools to improve the understanding and diagnosis of dementia. The main focus of my PhD has been to develop image synthesis algorithms for MR-based attenuation correction in hybrid PET/MR scanners, and for detecting pathological abnormalities in the reconstructed PET data (more details here). During my one-year postdoc  at UCL I decided to tackle the problem of automatic MR-based radiotherapy treatment planning in the pelvic region by developing a new framework combining segmentation and image synthesis (more details here).

You can check my CV here.

Representative publications

  • Bottani, S., Burgos, N., Maire, A., Wild, A., Ströer, S., Dormont, D., and Colliot, O.: Automatic Quality Control of Brain T1-Weighted Magnetic Resonance Images for a Clinical Data Warehouse. Medical Image Analysis, 102219, 2021. doi:10.1016/j.media.2021.102219 Available on HAL
  • 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, 2020. doi:10.1117/12.2548952 Available on HAL
  • Routier, A., Burgos, N., Díaz, M., Bacci, M., Bottani, S., El-Rifai, O., Fontanella, S., Gori, P., Guillon, J., Guyot, A., Hassanaly, R., Jacquemont, T., Lu, P., Marcoux, A., Moreau, T., Samper-González, J., Teichmann, M., Thibeau-Sutre, E., Vaillant, G., Wen, J., Wild, A., Habert, M.-O., Durrleman, S., and Colliot, O.: Clinica: An Open-Source Software Platform for Reproducible Clinical Neuroscience Studies. Frontiers in Neuroinformatics, 15: 39, 2021. doi:10.3389/fninf.2021.689675 Available on HAL
  • Wen, J., Thibeau-Sutre, E., Diaz-Melo, M., Samper-González, J., Routier, A., Bottani, S., Dormont, D., 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 Available on HAL
  • Samper-González, J., Burgos, N., Bottani, S., Fontanella, S., Lu, P., Marcoux, A., Routier, A., Guillon, J., Bacci, M., Wen, J., Bertrand, A., Bertin, H., Habert, M.-O., Durrleman, S., Evgeniou, T., and Colliot, O.: Reproducible Evaluation of Classification Methods in Alzheimer’s Disease: Framework and Application to MRI and PET Data. NeuroImage, 183: 504–521, 2018. doi:10.1016/j.neuroimage.2018.08.042 Available on HAL
  • 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 Available on HAL
  • Burgos, N., Guerreiro, F., McClelland, J., Presles, B., Modat, M., Nill, S., Dearnaley, D., deSouza, N., Oelfke, U., Knopf, A.-C., Ourselin, S., and Cardoso, M.J.: Iterative Framework for the Joint Segmentation and CT Synthesis of MR Images: Application to MRI-Only Radiotherapy Treatment Planning. Physics in Medicine and Biology, 62(11): 4237, 2017. doi:10.1088/1361-6560/aa66bf An invited paper in the Special Issues of Physics in Medicine and Biology on Recent Progress in Applications of Computing to Radiotherapy, 62(11): 4237, 2017
  • 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