Research

My main research interests are machine learning (ML), statistical learning and computer vision applied to ecological transformation. My former research field was on a medical image analysis.

Machine Learning & Image Processing for In-vivo Microscopy

Project: Motion Aware Mosaicing project page

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References:

J. Mahé, N. Linard, M. Kohandani Tafreshi, T. Vercauteren, N. Ayache, F. Lacombe, R. Cuingnet, Motion-Aware Mosaicing for Confocal Laser Endomicroscopy, In Medical Image Computing and Computer-Assisted Intervention--MICCAI 2015 (pp. 447-454). Springer International Publishing [poster].

Machine Learning for Detection & Segmentation

Project: Shape & Appearance Inference in image segmentation

References:

R. Prevost, R. Cuingnet, B. Mory, L.D. Cohen, R. Ardon, Tagged Template Deformation, Medical Image Computing and Computer-Assisted Intervention–MICCAI 2014, 674-681 [poster].

R. Prevost, R. Cuingnet, B. Mory, L.D. Cohen, R. Ardon, Incorporating shape variability in image segmentation via implicit template deformation, In Medical Image Computing and Computer-Assisted Intervention–MICCAI 2013 (pp. 82-89). Springer Berlin Heidelberg [oral, speaker R. Prevost].

R. Prevost, R. Cuingnet, B. Mory, J.-M. Correas, L.D. Cohen, R. Ardon, Joint Co-Segmentation and Registration of 3D Ultrasound Images, Proceedings of the International Conference on Information Processing in Medical Imaging (IPMI), 2013, LNCS 7917: 268-279 [poster].

Project: MULTI-ORGAN Detection & Segmentation (CT / MRI)

References:

R. Gauriau, R. Cuingnet, D. Lesage, I. Bloch, Multi-organ Localization Combining Global-to-Local Regression and Confidence Maps, Medical Image Computing and Computer-Assisted Intervention–MICCAI 2014, 337-344 [poster].

(Supplementary Material)

R. Gauriau, R. Cuingnet, R. Prevost, B. Mory, R. Ardon, D. Lesage, I. Bloch, A generic, robust and fully-automatic workflow for 3D CT liver segmentation, Proceedings of the 5 th Workshop on Computational and Clinical Applications in Abdominal Imaging in conjunction with MICCAI 2013. In Abdominal Imaging. Computation and Clinical Applications (pp. 241-250). Springer Berlin Heidelberg [oral, speaker R. Gauriau].

R. Cuingnet, R. Prevost, D. Lesage, L.D. Cohen, B. Mory, R. Ardon, Automatic Detection and Segmentation of Kidneys in 3D CT Images Using Random Forests, Medical Image Computing Computer Assisted Intervention, 2012 [oral].

R. Gauriau, R. Cuingnet, D. Lesage, I. Bloch, Multi-Organ Localization with Cascaded Global-to-Local Regression and Shape Prior, Medical Image Analysis, 2015, 23 (1): 70-83

Project: Image Co-Segmentation & Registration

References:

R. Prevost, B. Romain, R. Cuingnet, B. Mory, L. Rouet, O. Lucidarme, L.D. Cohen, R. Ardon, Registration of Free-Breathing 3D+t Abdominal Perfusion CT Images via Co-Segmentation, Medical Image Computing Computer Assisted Intervention, 2013 [poster] to appear.

R. Prevost, R. Cuingnet, B. Mory, J.-M. Correas, L.D. Cohen, R. Ardon, Joint Co-Segmentation and Registration of 3D Ultrasound Images, Proceedings of the International Conference on Information Processing in Medical Imaging (IPMI), 2013, LNCS 7917: 268-279 [poster].

Machine Learning for Neuroimaging Data Analysis

Project: MRI Classification (Alzheimer’s Disease): Review & Comparisons

SVM, Shape Description, Comparisons

References:

R. Cuingnet, É. Gerardin, J. Tessieras, G. Auzias, S. Lehéricy, M.-O. Habert, M. Chupin, H. Benali, O. Colliot and the Alzheimer's Disease Neuroimaging Initiative, Automatic classification of patients with Alzheimer's disease from structural MRI: A comparison of ten methods using the ADNI database, NeuroImage, 2011, 6 (2): 766-781.

(Supplementary Material)

É. Gerardin; G. Chételat, M. Chupin, R. Cuingnet, B. Desgranges, H.S. Kim, M. Niethammer, B. Dubois, S Lehéricy, L. Garnero, F. Eustache, O. Colliot, and the Alzheimer’s Disease Neuroimaging Initiative, Multidimensional classification of hippocampal shape features discriminates Alzheimer's disease and mild cognitive impairment from normal aging, NeuroImage, 2009, 47 (4): 1476-1486.

Project: Spatial and anatomical regularization of SVM

References:

R. Cuingnet, J. A. Glaunès, M. Chupin, H. Benali, O. Colliot and the Alzheimer’s Disease Neuroimaging Initiative, Spatial and anatomical regularization of SVM: a general framework for neuroimaging data, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(3), 682-696.

R. Cuingnet, J. A. Glaunès, M. Chupin, H. Benali and O. Colliot, Anatomical Regularization on Statistical Manifolds for the Classification of Patients with Alzheimer’s Disease, Proceedings of the Second International Workshop on Machine Learning in Medical Imaging (MLMI) in conjunction with MICCAI 2011, LNCS, 2011, 7009: 201-208 [oral (acceptance rate 20%)].

R. Cuingnet, M. Chupin, H. Benali and O. Colliot, Spatial and anatomical regularization of SVM for brain image analysis, Proceedings of the Neural Information Processing Systems conference (NIPS 2010), 2010, pages 460-468 [poster (acceptance rate 24%)— Travel Award].

Project: Permutation tests based on SVM weights for interpretation of Linear SVM models

References:

R. Cuingnet, C. Rosso, S. Lehéricy, D. Dormont, H. Benali, Y. Samson and O. Colliot, Spatially regularized SVM for the detection of brain areas associated with stroke outcome, Medical Image Computing Computer Assisted Intervention, 2010, LNCS 13 (Pt 1):316-23 [oral (acceptance rate 6%) – Young Scientist Award].

R. Cuingnet, C. Rosso, M. Chupin, S. Lehéricy, D. Dormont, H. Benali, Y. Samson and O. Colliot, Spatial regularization of SVM for the detection of diffusion alterations associated with stroke outcome, Medical Image Analysis, 2011, 15 (5): 729-737 [invited paper].

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