Publications

International journal publications

2022

  • 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, 75: 102219, 2022. doi:10.1016/j.media.2021.102219 Available on HAL
  • Thibeau-Sutre, E., Díaz, M., Hassanaly, R., Routier, A., Dormont, D., Colliot, O., and Burgos, N.: ClinicaDL: An Open-Source Deep Learning Software for Reproducible Neuroimaging Processing. Computer Methods and Programs in Biomedicine, 220: 106818, 2022. doi:10.1016/j.cmpb.2022.106818 Available on HAL
  • Chadebec, C., Thibeau-Sutre, E., Burgos, N., and Allassonnière, S.: Data Augmentation in High Dimensional Low Sample Size Setting Using a Geometry-Based Variational Autoencoder. IEEE Transactions on Pattern Analysis and Machine Intelligence, , 1–18, 2022. doi:10.1109/TPAMI.2022.3185773 Available on HAL
  • Epelbaum, S., Burgos, N., Canney, M., Matthews, D., Houot, M., Santin, M.D., Desseaux, C., Bouchoux, G., Stroer, S., Martin, C., Habert, M.-O., Levy, M., Bah, A., Martin, K., Delatour, B., Riche, M., Dubois, B., Belin, L., and Carpentier, A.: Pilot Study of Repeated Blood-Brain Barrier Disruption in Patients with Mild Alzheimer’s Disease with an Implantable Ultrasound Device. Alzheimer’s Research & Therapy, 14(1): 40, 2022. doi:10.1186/s13195-022-00981-1 Available on HAL

2021

  • Burgos*, N., Bottani*, S., Faouzi*, J., Thibeau-Sutre*, E., and Colliot, O.: Deep Learning for Brain Disorders: From Data Processing to Disease Treatment. Briefings in Bioinformatics, 22(2): 1560–1576, 2021 (*: joint first authorship). doi:10.1093/bib/bbaa310 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
  • 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
  • Ansart, M., Epelbaum, S., Bassignana, G., Bône, A., Bottani, S., Cattai, T., Couronné, R., Faouzi, J., Koval, I., Louis, M., Thibeau-Sutre, E., Wen, J., Wild, A., Burgos, N., Dormont, D., Colliot, O., and Durrleman, S.: Predicting the Progression of Mild Cognitive Impairment Using Machine Learning: A Systematic, Quantitative and Critical Review. Medical Image Analysis, 67: 101848, 2021. doi:10.1016/j.media.2020.101848 Available on HAL
  • Koval, I., Bône, A., Louis, M., Lartigue, T., Bottani, S., Marcoux, A., Samper-González, J., Burgos, N., Charlier, B., Bertrand, A., Epelbaum, S., Colliot, O., Allassonnière, S., and Durrleman, S.: AD Course Map Charts Alzheimer’s Disease Progression. Scientific Reports, 11(1): 8020, 2021. doi:10.1038/s41598-021-87434-1 Available on HAL

2020

  • Burgos, N., and Colliot, O.: Machine Learning for Classification and Prediction of Brain Diseases: Recent Advances and Upcoming Challenges. Current Opinion in Neurology, 33(4): 439–450, 2020. doi:10.1097/WCO.0000000000000838 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
  • Couvy-Duchesne, B., Faouzi, J., Martin, B., Thibeau–Sutre, E., Wild, A., Ansart, M., Durrleman, S., Dormont, D., Burgos, N., and Colliot, O.: Ensemble Learning of Convolutional Neural Network, Support Vector Machine, and Best Linear Unbiased Predictor for Brain Age Prediction: ARAMIS Contribution to the Predictive Analytics Competition 2019 Challenge. Frontiers in Psychiatry, 11, Frontiers, 2020. doi:10.3389/fpsyt.2020.593336 Available on HAL
  • Wen, J., Samper-González, J., Bottani, S., Routier, A., Burgos, N., Jacquemont, T., Fontanella, S., Durrleman, S., Epelbaum, S., Bertrand, A., and Colliot, O.: Reproducible Evaluation of Diffusion MRI Features for Automatic Classification of Patients with Alzheimer’s Disease. Neuroinformatics, , 2020. doi:10.1007/s12021-020-09469-5 Available on HAL

2018

  • 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
  • Marcoux, A., Burgos, N., Bertrand, A., Teichmann, M., Routier, A., Wen, J., Samper-Gonzalez, J., Bottani, S., Durrleman, S., Habert, M.-O., and Colliot, O.: An Automated Pipeline for the Analysis of PET Data on the Cortical Surface. Frontiers in Neuroinformatics, 12, 2018. doi:10.3389/fninf.2018.00094
  • Arabi, H., Dowling, J.A., Burgos, N., Han, X., Greer, P.B., Koutsouvelis, N., and Zaidi, H.: Comparative Study of Algorithms for Synthetic CT Generation from MRI: Consequences for MRI-Guided Radiation Planning in the Pelvic Region. Medical Physics, 0(ja)2018. doi:10.1002/mp.13187
  • Kieselmann, J.P., Kamerling, C.P., Burgos, N., Menten, M.J., Fuller, C.D., Nill, S., Cardoso, M.J., and Oelfke, U.: Geometric and Dosimetric Evaluations of Atlas-Based Segmentation Methods of MR Images in the Head and Neck Region. Physics in Medicine and Biology, 2018. doi:10.1088/1361-6560/aacb65
  • Scott, C.J., Jiao, J., Melbourne, A., Burgos, N., Cash, D.M., De Vita, E., Markiewicz, P.J., O’Connor, A., Thomas, D.L., Weston, P.S., Schott, J.M., Hutton, B.F., and Ourselin, S.: Reduced Acquisition Time PET Pharmacokinetic Modelling Using Simultaneous ASL–MRI: Proof of Concept. Journal of Cerebral Blood Flow & Metabolism, , September, 0271678X18797343, 2018. doi:10.1177/0271678X18797343

2017

  • 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
  • Guerreiro*, F., Burgos*, N., Dunlop, A., Wong, K., Petkar, I., Nutting, C., Harrington, K., Bhide, S., Newbold, K., Dearnaley, D., deSouza, N.M., Morgan, V.A., McClelland, J., Nill, S., Cardoso, M.J., Ourselin, S., Oelfke, U., and Knopf, A.C.: Evaluation of a Multi-Atlas CT Synthesis Approach for MRI-Only Radiotherapy Treatment Planning. Physica Medica, 35: 7–17, 2017 (*: joint first authorship). doi:10.1016/j.ejmp.2017.02.017
  • Ladefoged, C.N., Law, I., Anazodo, U., St. Lawrence, K., Izquierdo-Garcia, D., Catana, C., Burgos, N., Cardoso, M.J., Ourselin, S., Hutton, B., Mérida, I., Costes, N., Hammers, A., Benoit, D., Holm, S., Juttukonda, M., An, H., Cabello, J., Lukas, M., Nekolla, S., Ziegler, S., Fenchel, M., Jakoby, B., Casey, M.E., Benzinger, T., Højgaard, L., Hansen, A.E., and Andersen, F.L.: A Multi-Centre Evaluation of Eleven Clinically Feasible Brain PET/MRI Attenuation Correction Techniques Using a Large Cohort of Patients. NeuroImage, 147: 346–359, 2017. doi:10.1016/j.neuroimage.2016.12.010
  • Lane, C.A., Parker, T.D., Cash, D.M., Macpherson, K., Donnachie, E., Murray-Smith, H., Barnes, A., Barker, S., Beasley, D.G., Bras, J., Brown, D., Burgos, N., Byford, M., Jorge Cardoso, M., Carvalho, A., Collins, J., De Vita, E., Dickson, J.C., Epie, N., Espak, M., Henley, S.M.D., Hoskote, C., Hutel, M., Klimova, J., Malone, I.B., Markiewicz, P., Melbourne, A., Modat, M., Schrag, A., Shah, S., Sharma, N., Sudre, C.H., Thomas, D.L., Wong, A., Zhang, H., Hardy, J., Zetterberg, H., Ourselin, S., Crutch, S.J., Kuh, D., Richards, M., Fox, N.C., and Schott, J.M.: Study Protocol: Insight 46 – a Neuroscience Sub-Study of the MRC National Survey of Health and Development. BMC Neurology, 17: 75, 2017. doi:10.1186/s12883-017-0846-x
  • Jiao, J., Bousse, A., Thielemans, K., Burgos, N., Weston, P.S.J., Schott, J.M., Atkinson, D., Arridge, S.R., Hutton, B.F., Markiewicz, P., and Ourselin, S.: Direct Parametric Reconstruction with Joint Motion Estimation/Correction for Dynamic Brain PET Data. IEEE Transactions on Medical Imaging, 36(1): 203–213, 2017. doi:10.1109/TMI.2016.2594150

2016

  • Sekine, T., Burgos, N., Warnock, G., Huellner, M., Buck, A., Voert, E.E.G.W. ter, Cardoso, M.J., Hutton, B.F., Ourselin, S., Veit-Haibach, P., and Delso, G.: Multi Atlas-Based Attenuation Correction for Brain FDG- PET Imaging Using a TOF-PET/MR Scanner: Comparison with Clinical Single Atlas- and CT-Based Attenuation Correction. Journal of Nuclear Medicine, 57(8): 1258–1264, 2016. doi:10.2967/jnumed.115.169045

2015

  • Burgos, N., Cardoso, M.J., Thielemans, K., Modat, M., Dickson, J., Schott, J.M., Atkinson, D., Arridge, S.R., Hutton, B.F., and Ourselin, S.: Multi-Contrast Attenuation Map Synthesis for PET/MR Scanners: Assessment on FDG and Florbetapir PET Tracers. European Journal of Nuclear Medicine and Molecular Imaging, 42(9): 1447–1458, 2015. doi:10.1007/s00259-015-3082-x
  • Zuluaga*, M.A., Burgos*, N., Mendelson, A.F., Taylor, A.M., and Ourselin, S.: Voxelwise Atlas Rating for Computer Assisted Diagnosis: Application to Congenital Heart Diseases of the Great Arteries. Medical Image Analysis, 26(1): 185–194, 2015 (*: joint first authorship). doi:10.1016/j.media.2015.09.001
  • Kochan, M., Daga, P., Burgos, N., White, M., Cardoso, M.J., Mancini, L., Winston, G.P., McEvoy, A.W., Thornton, J., Yousry, T., Duncan, J.S., Stoyanov, D., and Ourselin, S.: Simulated Field Maps for Susceptibility Artefact Correction in Interventional MRI. International Journal of Computer Assisted Radiology and Surgery, 10(9): 1405–1416, 2015. doi:10.1007/s11548-015-1253-7
  • Weston, P.S.J., Paterson, R.W., Modat, M., Burgos, N., Cardoso, M.J., Magdalinou, N., Lehmann, M., Dickson, J.C., Barnes, A., Bomanji, J.B., Kayani, I., Cash, D.M., Ourselin, S., Toombs, J., Lunn, M.P., Mummery, C.J., Warren, J.D., Rossor, M.N., Fox, N.C., Zetterberg, H., and Schott, J.M.: Using Florbetapir Positron Emission Tomography to Explore Cerebrospinal Fluid Cut Points and Gray Zones in Small Sample Sizes. Alzheimer’s & Dementia: Diagnosis, Assessment & Disease Monitoring, 1(4): 440–446, 2015. doi:10.1016/j.dadm.2015.10.001

2014

  • 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

Book

  • Burgos, N., Svoboda, D., eds.: Biomedical Image Synthesis Simulation: Methods and Applications, MICCAI Book series, Elsevier, 2022. doi:10.1016/C2020‑0‑01250‑8

Book chapters


2022

  • Svoboda, D., and Burgos, N.: Introduction to Medical and Biomedical Image Synthesis. In Biomedical Image Synthesis and Simulation, edited by Burgos, N. and Svoboda, D., The MICCAI Society Book Series, 2022. doi:10.1016/B978-0-12-824349-7.00008-6 Available on HAL
  • Burgos, N.: Medical Image Synthesis Using Segmentation and Registration. In Biomedical Image Synthesis and Simulation, edited by Burgos, N. and Svoboda, D., The MICCAI Society Book Series, 2022. doi:10.1016/B978-0-12-824349-7.00011-6 Available on HAL
  • Nečasová, T., Burgos, N., and Svoboda, D.: Validation and Evaluation Metrics for Medical and Biomedical Image Synthesis. In Biomedical Image Synthesis and Simulation, edited by Burgos, N. and Svoboda, D., The MICCAI Society Book Series, 2022. doi:10.1016/B978-0-12-824349-7.00032-3 Available on HAL
  • Burgos, N., Tsaftaris, S.A., and Svoboda, D.: Future Trends in Medical and Biomedical Image Synthesis. In Biomedical Image Synthesis and Simulation, edited by Burgos, N. and Svoboda, D., The MICCAI Society Book Series, 2022. doi:10.1016/B978-0-12-824349-7.00034-7 Available on HAL

Conference proceedings

2021

  • Svoboda, D., Burgos, N., Wolterink, J.M., and Zhao, C., eds.: Simulation and Synthesis in Medical Imaging: 6th International Workshop, SASHIMI 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, September 27, 2021 . Springer International Publishing, 2021. doi:10.1007/978‐3‐030‐87592‐3

2020

  • Burgos, N., Svoboda, D., Wolterink, J.M., and Zhao, C., eds.: Simulation and Synthesis in Medical Imaging: 5th International Workshop, SASHIMI 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4, 2020 . Springer International Publishing, 2020. doi:10.1007/978-3-030-59520-3

2019

  • Burgos, N., Gooya, A., and Svoboda, D., eds.: Fourth International Workshop on Simulation and Synthesis in Medical Imaging, SASHIMI 2019, held in conjunction with MICCAI 2019, Shenzhen, China, October 2019. Springer International Publishing, 2019. doi:10.1007/978-3-030-32778-1

2018

  • Gooya, A., Goksel, O., Oguz, I., and Burgos, N., eds.: Third International Workshop on Simulation and Synthesis in Medical Imaging, SASHIMI 2018, held in conjunction with MICCAI 2018, Granada, Spain, September 2018. Springer International Publishing, 2018. doi:10.1007/978-3- 030-00536-8

Conferences with full-length peer-reviewed proceedings

2023

  • Hassanaly, R., Bottani, S., Sauty, B., Colliot, O., and Burgos, N.: Simulation-Based Evaluation Framework for Deep Learning Unsupervised Anomaly Detection on Brain FDG PET. In SPIE Medical Imaging 2023, 2023. Available on HAL
  • Loizillon, S., Bottani, S., Maire, A., Ströer, S., Dormont, D., Colliot, O., and Burgos, N.: Transfer Learning from Synthetic to Routine Clinical Data for Motion Artefact Detection in Brain T1-Weighted MRI. In SPIE Medical Imaging 2023, 2023. Available on HAL

2022

  • Thibeau-Sutre, E., Couvy-Duchesne, B., Dormont, D., Colliot, O., Burgos, N.: MRI field strength predicts Alzheimer’s disease: A case example of bias in the ADNI data set. In 2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI), 2022. doi:10.1109/ISBI52829.2022.9761504 Available on HAL
  • Bottani, S., Thibeau-Sutre, E., Maire, A., Ströer, S., Dormont, D., Colliot, O., Burgos, N.: Homogenization of brain MRI from a clinical data warehouse using contrast‐enhanced to non‐contrast‐enhanced image translation with U‐Net derived models. In SPIE Medical Imaging 2022, 2022. doi:10.1117/12.2608565 Available on HAL

2020

  • 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

2019

  • Samper-Gonzalez, J., Burgos, N., Bottani, S., Habert, M.-O., Evgeniou, T., Epelbaum, S., and Colliot, O.: Reproducible Evaluation of Methods for Predicting Progression to Alzheimer’s Disease from Clinical and Neuroimaging Data. In SPIE Medical Imaging 2019, 2019. doi:10.1117/12.2512430 Available on HAL

2017

  • Burgos, N., Samper-González, J., Bertrand, A., Habert, M.-O., Ourselin, S., Durrleman, S., Cardoso, M.J., and Colliot, O.: Individual Analysis of Molecular Brain Imaging Data through Automatic Identification of Abnormality Patterns. In Molecular Imaging, Reconstruction and Analysis of Moving Body Organs, and Stroke Imaging and Treatment, LNCS, 10555: 13–22, Springer, 2017. doi:10.1007/978-3-319-67564-0_2
  • Samper-González, J., Burgos, N., Fontanella, S., Bertin, H., Habert, M.-O., Durrleman, S., Evgeniou, T., and Colliot, O.: Yet Another ADNI Machine Learning Paper? Paving the Way towards Fully-Reproducible Research on Classification of Alzheimer’s Disease. In Machine Learning in Medical Imaging, LNCS, 10541: 53–60, Springer, 2017. doi:10.1007/978-3-319-67389-9_7
  • Scott, C.J., Jiao, J., Cardoso, M.J., Melbourne, A., De Vita, E., Thomas, D.L., Burgos, N., Markiewicz, P., Schott, J.M., Hutton, B.F., and Ourselin, S.: Short Acquisition Time PET Quantification Using MRI-Based Pharmacokinetic Parameter Synthesis. In Medical Image Computing and Computer-Assisted Intervention − MICCAI 2017, LNCS, 10434: 737–744, Springer, 2017. doi:10.1007/978-3-319-66185-8_83

2016

  • Burgos, N., Guerreiro, F., McClelland, J., Nill, S., Dearnaley, D., deSouza, N., Oelfke, U., Knopf, A.-C., Ourselin, S., and Cardoso, M.J.: Joint Segmentation and CT Synthesis for MRI-Only Radiotherapy Treatment Planning. In Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016, LNCS, 9901: 547–555, Springer, 2016. doi:10.1007/978-3-319-46723-8_63

2015

  • Burgos, N., Cardoso, M.J., Guerreiro, F., Veiga, C., Modat, M., McClelland, J., Knopf, A.-C., Punwani, S., Atkinson, D., Arridge, S.R., Hutton, B.F., and Ourselin, S.: Robust CT Synthesis for Radiotherapy Planning: Application to the Head & Neck Regio. In Medical Image Computing and Computer-Assisted Intervention — MICCAI 2015, LNCS, 9350: 476–484, Springer, 2015. doi:10.1007/978-3-319-24571-3_57
  • Burgos, N., Cardoso, M.J., Mendelson, A.F., Schott, J.M., Atkinson, D., Arridge, S.R., Hutton, B.F., and Ourselin, S.: Subject-Specific Models for the Analysis of Pathological FDG PET Data. In Medical Image Computing and Computer-Assisted Intervention — MICCAI 2015, LNCS, 9350: 651–658, Springer, 2015. doi:10.1007/978-3-319-24571-3_78
  • Zuluaga*, M.A., Burgos*, N., Taylor, A.M., and Ourselin, S.: Multi-Atlas Synthesis for Computer Assisted Diagnosis: Application to Cardiovascular Diseases. In 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI), 290–293, 2015 (*: joint first authorship). doi:10.1109/ISBI.2015.7163870
  • Jiao, J., Markiewicz, P., Burgos, N., Atkinson, D., Hutton, B., Arridge, S., and Ourselin, S.: Detail-Preserving PET Reconstruction with Sparse Image Representation and Anatomical Priors. In Information Processing in Medical Imaging, LNCS, 9123: 540–551, Springer, 2015. doi:10.1007/978-3-319-19992-4_42

2014

  • Burgos, N., Thielemans, K., Cardoso, M.J., Markiewicz, P., Jiao, J., Dickson, J., Duncan, J.S., Atkinson, D., Arridge, S.R., Hutton, B.F., and Ourselin, S.: Effect of Scatter Correction When Comparing Attenuation Maps: Application to Brain PET/MR. In 2014 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC), 1–5, 2014. doi:10.1109/NSSMIC.2014.7430775
  • Jiao, J., Bousse, A., Thielemans, K., Markiewicz, P., Burgos, N., Atkinson, D., Arridge, S., Hutton, B.F., and Ourselin, S.: Joint Parametric Reconstruction and Motion Correction Framework for Dynamic PET Data. In Medical Image Computing and Computer-Assisted Intervention – MICCAI 2014, LNCS, 8673: 114–121, Springer, 2014. doi:10.1007/978-3-319-10404-1_15
  • Kochan, M., Daga, P., Burgos, N., White, M., Cardoso, M.J., Mancini, L., Winston, G.P., McEvoy, A.W., Thornton, J., Yousry, T., Duncan, J.S., Stoyanov, D., and Ourselin, S.: Simulated Field Maps: Toward Improved Susceptibility Artefact Correction in Interventional MRI. In Information Processing in Computer-Assisted Interventions, LNCS, 8498: 226–235, Springer, 2014. doi:10.1007/978-3-319-07521-1_24

2013

  • Burgos, N., Cardoso, M.J., Modat, M., Pedemonte, S., Dickson, J., Barnes, A., Duncan, J.S., Atkinson, D., Arridge, S.R., Hutton, B.F., and Ourselin, S.: Attenuation Correction Synthesis for Hybrid PET-MR Scanners. In Medical Image Computing and Computer-Assisted Intervention – MICCAI 2013, LNCS, 8149: 147–154, Springer, 2013. doi:10.1007/978-3-642-40811-3_19

Conferences abstracts

2022

  • El Rifai, O., Melo, M.D., Hassanaly, R., Joulot, M., Routier, A.M., Thibeau-Sutre, E., Vaillant, G., Durrleman, S., Burgos, N., and Colliot, O.: Advances in the Clinica Software Platform for Clinical Neuroimaging Studies. In Annual Meeting of the Organization for Human Brain Mapping – OHBM 2022, 2022. Available on HAL
  • Thibeau-Sutre, E., Díaz, M., Hassanaly, R., Colliot, O., and Burgos, N.: A Glimpse of ClinicaDL, an Open-Source Software for Reproducible Deep Learning in Neuroimaging. In Medical Imaging with Deep Learning, 2022. Available on Open Review

2020

  • Routier, A., Marcoux, A., Melo, M.D., Samper-González, J., Wild, A., Guyot, A., Wen, J., Thibeau-Sutre, E., Bottani, S., Durrleman, S., Burgos, N., and Colliot, O.: New Longitudinal and Deep Learning Pipelines in the Clinica Software Platform. In Annual Meeting of the Organization for Human Brain Mapping – OHBM 2020, 2020. Available on HAL
  • Cash, D.M., Markiewicz, P.J., Jiao, J., Coath, W., Modat, M., Lane, C.A., Parker, T.D., Keuss, S.E., Buchanan, S.M., Burgos, N., Dickson, J., Barnes, A., Cardoso, J., Alves, I.L., Barkhof, F., Thomas, D.L., Beasley, D., Wong, A., Schöll, M., Richards, M., Ourselin, S., Fox, N.C., and Schott, J.M.: Comparison of Static and Dynamic Analysis Techniques for Longitudinal Analysis of Amyloid PET. In Alzheimer’s Association International Conference – AAIC 2020, 2020.

2019

  • Wen, J., Thibeau–Sutre, E., Samper-González, J., Routier, A., Bottani, S., Dormont, D., Durrleman, S., Colliot, O., and Burgos, N.: How Serious Is Data Leakage in Deep Learning Studies on Alzheimer’s Disease Classification?. In Annual Meeting of the Organization for Human Brain Mapping – OHBM 2019, 2019. Available on HAL
  • Wen, J., Samper-González, J., Routier, A., Bottani, S., Durrleman, S., Burgos, N., and Colliot, O.: Beware of Feature Selection Bias! Example on Alzheimer’s Disease Classification from Diffusion MRI. In Annual Meeting of the Organization for Human Brain Mapping – OHBM 2019, 2019. Available on HAL
  • Routier, A., Marcoux, A., Diaz Melo, M., Guillon, J., Samper-Gonzlez, J., Wen, J., Bottani, S., Guyot, A., Thibeau–Sutre, E., Teichmann, M., Habert, M.-O., Durrleman, S., Burgos, N., and Colliot, O.: New Advances in the Clinica Software Platform for Clinical Neuroimaging Studies. In Annual Meeting of the Organization for Human Brain Mapping – OHBM 2019, 2019. Available on HAL
  • Samper-Gonzalez, J., Burgos, N., Bottani, S., Habert, M.-O., Evgeniou, T., Epelbaum, S., and Colliot, O.: Predicting Progression to Alzheimer’s Disease from Clinical and Imaging Data: A Reproducible Study. In Annual Meeting of the Organization for Human Brain Mapping – OHBM 2019, 2019.  Available on HAL
  • Ansart, M., Burgos, N., Colliot, O., Dormont, D., and Durrleman, S.: Prediction of Future Cognitive Scores and Dementia Onset in Mild Cognitive Impairment Patients. In Annual Meeting of the Organization for Human Brain Mapping – OHBM 2019, 2019. Available on HAL
  • Koval, I., Marcoux, A., Burgos, N., Allassonnière, S., Colliot, O., and Durrleman, S.: Deciphering the Progression of PET Alterations Using Surface-Based Spatiotemporal Modeling. In Annual Meeting of the Organization for Human Brain Mapping – OHBM 2019, 2019. Available on HAL
  • Cash, D.M., Modat, M., Coath, W., Cardoso, M.J., Markiewicz, P., Lane, C.A., Parker, T., Keuss, S., Buchanan, S., Burgos, N., Dickson, J., Barnes, A., Thomas, D.L., Beasley, D., Malone, I.B., Erlandsson, K., Thomas, B.A., Ourselin, S., Fox, N.C., Schott, J.M., and Richards, M.: Longitudinal Rates of Amyloid Accumulation in a 70-Year Old British Birth Cohort. In Alzheimer’s & Dementia, 2019
  • Coath, W., Modat, M., Cardoso, M.J., Markiewicz, P., Lane, C.A., Parker, T., Keuss, S., Buchanan, S., Burgos, N., Dickson, J., Barnes, A., Thomas, D.L., Beasley, D., Malone, I.B., Wong, A., Thomas, B.A., Ourselin, S., Richards, M., Fox, N.C., Schott, J.M., and Cash, D.M.: Centiloid Scale Transformation of Florbetapir Data Acquired on a PET/MR Scanner. In Alzheimer’s & Dementia, 2019

2018

  • Marcoux, A., Burgos, N., Bertrand, A., Routier, A., Wen, J., Samper-Gonzalez, J., Bottani, S., Durrleman, S., Habert, M.-O., Colliot, O.: A pipeline for the analysis of 18F-FDG PET data on the cortical surface and its evaluation on ADNI. Presented at the Annual meeting of the Organization for Human Brain Mapping – OHBM 2018. Available on HAL.
  • Routier, A., Guillon, J., Burgos, N., Samper-Gonzalez, J., Wen, J., Fontanella, S., Bottani, S., Jacquemont, T., Marcoux, A., Gori, P., Lu, P., Moreau, T., Bacci, M., Durrleman, S., Colliot, O.: Clinica: an open source software platform for reproducible clinical neuroscience studies. Presented at the Annual meeting of the Organization for Human Brain Mapping – OHBM 2018. Available on HAL.
  • Samper-Gonzalez, J., Bottani, S., Burgos, N., 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., Colliot, O.: Reproducible evaluation of Alzheimer’s Disease classification from MRI and PET data. Presented at the Annual meeting of the Organization for Human Brain Mapping – OHBM 2018. Available on HAL.
  • Wen, J., Samper-Gonzalez, J., Bottani, S., Routier, A., Burgos, N., Jacquemont, T., Fontanella, S., Durrleman, S., Bertrand, A., Colliot, O.: Comparison of DTI Features for the Classification of Alzheimer’s Disease: A Reproducible Study. Presented at the Annual meeting of the Organization for Human Brain Mapping – OHBM 2018. Available on HAL.
  • Wen, J., Samper-Gonzalez, J., Bottani, S., Routier, A., Burgos, N., Jacquemont, T., Fontanella, S., Durrleman, S., Bertrand, A., Colliot, O.: Using diffusion MRI for classification and prediction of Alzheimer’s Disease: a reproducible study. Presented at the Alzheimer’s Association International Conference – AAIC 2018. Available on HAL.

2017

  • Burgos, N., Samper-González, J., Bertrand, A., Habert, M.-O., Ourselin, S., Durrleman, S., Cardoso, M.J., and Colliot, O.: Diagnosis of Alzheimer’s Disease through Identification of Abnormality Patterns in FDG PET Data. In Proceedings of the 30th Annual Congress of the European Association of Nuclear Medicine (EANM), S253–S254, Springer, 2017. doi:10.1007/s00259-017-3822-1
  • Burgos, N., Samper-González, J., Cardoso, M.J., Durrleman, S., Ourselin, S., and Colliot, O.: Early Diagnosis of Alzheimer’s Disease Using Subject-Specific Models of FDG-PET Data. Alzheimer’s & Dementia, 13(7): P1117, 2017. doi:10.1016/j.jalz.2017.06.1618
  • Cash, D.M., Burgos, N., Modat, M., Dickson, J., Beasley, D., Markiewicz, P., Lane, C.A., Parker, T., Barnes, A., Thomas, D.L., Cardoso, M.J., Malone, I.B., Veale, T., Wallon, D., Klimova, J., Erlandsson, K., Wong, A., Richards, M., Fox, N.C., Ourselin, S., and Schott, J.M.: A Comparison of Techniques for Quantifying Amyloid Burden on a Combined PET/MR Scanner. Alzheimer’s & Dementia, 13(7): P12–P13, 2017. doi:10.1016/j.jalz.2017.06.2276
  • Schott, J.M., Cash, D.M., Lane, C.A., Parker, T., Burgos, N., Modat, M., Beasley, D., Dickson, J., Barnes, A., Thomas, D.L., Murray-Smith, H., Wong, A., Macpherson, K., James, S.-N., Cardoso, M.J., Malone, I.B., Klimova, J., Markiewicz, P., Crutch, S.J., Kuh, D., Ourselin, S., Richards, M., and Fox, N.C.: Exploring the Population Prevalence of β-Amyloid Burden: An Analysis of 250 Individuals Born in Mainland Britain in the Same Week in 1946. Alzheimer’s & Dementia, 13(7): P1088–P1089, 2017. doi:10.1016/j.jalz.2017.06.1563
  • James, S.-N., Parker, T., Lane, C.A., Cash, D.M., Wong, A., Barnes, A., Beasley, D., Burgos, N., Cardoso, M.J., Dickson, J., Klimova, J., Malone, I.B., Modat, M., Thomas, D.L., Kuh, D., Ourselin, S., Fox, N.C., Schott, J.M., and Richards, M.: Midlife Affective Symptoms Are Associated with Lower Brain Volumes in Later Life: Evidence from a Prospective UK Birth Cohort. Alzheimer’s & Dementia, 13(7): P212, 2017. doi:10.1016/j.jalz.2017.07.086

2016

  • Burgos, N., Cardoso, M.J., Guerreiro, F., McClelland, J., Knopf, A.-C., and Ourselin, S.: Simultaneous Organ-at-Risk Segmentation and CT Synthesis in the Pelvic Region for MRI-Only Radiotherapy Treatment Planning. In International Conference on the Use of Computers in Radiation Therapy (ICCR), 2016
  • Burgos, N., Cardoso, M.J., Guerreiro, F., McClelland, J., Knopf, A.-C., Punwani, and Ourselin, S.: CT Synthesis in the Head & Neck and Pelvic Regions for Radiotherapy Treatment Planning. In IPEM Workshop on MRI Guided Radiotherapy, 2016
  • Ladefoged, C.N., Law, I., Anazodo, U., Izquierdo-Garcia, D., Burgos, N., Mérida, I., Benoit, D., Juttukonda, M., Cabello, J., Fenchel, M., Jakoby, B., Højgaard, L., Hansen, A.E., and Andersen, F.L.: A Multi-Method, Multi-Center Study of PET/MRI Brain Attenuation Correction on a Large Cohort of [18F]- FDG Patients: Ready for Clinical Implementation. In Annual Meeting of the Radiological Society of North America (RSNA), 2016
  • Ladefoged, C.N., Law, I., Anazodo, U., St. Lawrence, K., Izquierdo-Garcia, D., Catana, C., Burgos, N., Cardoso, M.J., Hutton, B., Ourselin, S., Mérida, I., Costes, N., Hammers, A., Benoit, D., Holm, S., Juttukonda, M., An, H., Cabello, J., Lukas, M., Nekolla, S., Ziegler, S., Fenchel, M., Jakoby, B., Casey, M.E., Benzinger, T., Højgaard, L., Hansen, A.E., and Andersen, F.L.: A Multi-Centre Evaluation of Eleven Clinically Feasible Brain PET/MRI Attenuation Correction Techniques Using a Large Cohort of Patients. In 2016 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC), 2016
  • Prados Carrasco, F., Cardoso, M.J., Burgos, N., Wheeler-Kingshott, C.A.M., and Ourselin, S.: NiftyWeb: Web Based Platform for Image Processing on the Cloud. In Proceedings of the 24th Scientific Meeting and Exhibition of the International Society for Magnetic Resonance in Medicine (ISMRM), 2016. http://www.ismrm.org/2016-annual-meeting-exhibition/
  • Sekine, T., Burgos, N., Warnock, G., Huellner, M., Buck, A., Voert, E.E.G.W. ter, Cardoso, M.J., Hutton, B.F., Ourselin, S., Veit-Haibach, P., and Delso, G.: Multi Atlas-Based Attenuation Correction for Brain FDG- PET Imaging Using a TOF-PET/MR Scanner: Comparison with Clinical Single Atlas- and CT-Based Attenuation Correction. In Proceedings of the 24th Scientific Meeting and Exhibition of the International Society for Magnetic Resonance in Medicine (ISMRM), 2016

2015

  • Burgos, N., Cardoso, M.J., Modat, M., Punwani, S., Atkinson, D., Arridge, S.R., Hutton, B.F., and Ourselin, S.: CT Synthesis in the Head & Neck Region for PET/MR Attenuation Correction: An Iterative Multi-Atlas Approach. EJNMMI Physics, 2(1): A31, 2015. doi:10.1186/2197-7364-2-S1-A31
  • Dickson, J.C., Erlandsson, K., Lehmann, M., Modat, M., Burgos, N., Groves, A., and Schott, J.: Partial Volume Correction of Amyvid and FDG PET Data Using the Discrete Iterative Yang Technique. In Proceedings of the 28th Annual Congress of the European Association of Nuclear Medicine (EANM), S69, Springer, 2015. doi:10.1007/s00259-015-3198-z
  • Guerreiro, F., McClelland, J., Burgos, N., Cardoso, M.J., Dunlop, A., Wong, K., Nill, S., Oelfke, U., and Knopf, A.C.: Evaluation of Different Approaches to Obtain Synthetic CT Images for a MRI-Only Radiotherapy Workflow. In MR in RT Symposium, 2015
  • Mota, A., Cuplov, V., Schott, J., Hutton, B., Thielemans, K., Drobnjak, I., Dickson, J., Bert, J., Burgos, N., Cardoso, J., Modat, M., Ourselin, S., and Erlandsson, K.: Establishment of an Open Database of Realistic Simulated Data for Evaluation of Partial Volume Correction Techniques in Brain PET/MR. EJNMMI Physics, 2(1): A44, 2015. doi:10.1186/2197-7364-2-S1-A44

2014

  • Burgos, N., Cardoso, M.J., Thielemans, K., Duncan, J.S., Atkinson, D., Arridge, S.R., Hutton, B.F., and Ourselin, S.: Attenuation Correction Synthesis for Hybrid PET-MR Scanners: Validation for Brain Study Applications. EJNMMI Physics, 1(1): A52, 2014. doi:10.1186/2197-7364-1-S1-A52
  • Markiewicz, P., Thielemans, K., Burgos, N., Manber, R., Jiao, J., Barnes, A., Atkinson, D., Arridge, S.R., Hutton, B.F., and Ourselin, S.: Image Reconstruction of MMR PET Data Using the Open Source Software STIR. EJNMMI Physics, 1(1): A44, 2014. doi:10.1186/2197-7364-1-S1-A44

Thesis

2016

  • Burgos, N.: Image Synthesis for the Attenuation Correction and Analysis of PET/MR Data, Doctoral thesis, UCL (University College London), 2016. http://discovery.ucl.ac.uk/1517860/