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Start Over Author "Louis Collins, D." Topic [sdv.ib]life sciences [q-bio]/bioengineering
4 results on '"Louis Collins, D."'

1. Improving prediction of Alzheimer's disease using patterns of cortical thinning and homogenizing images according to disease stage

Author

Simon Eskildsen, Pierrick Coupé, Daniel García-Lorenzo, Vladimir Fonov, Pruessner, Jens C., Louis Collins, D., McConnell Brain Imaging Centre (MNI), Montreal Neurological Institute and Hospital, McGill University = Université McGill [Montréal, Canada]-McGill University = Université McGill [Montréal, Canada], Aarhus University [Aarhus], Laboratoire Bordelais de Recherche en Informatique (LaBRI), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB), Centre de Recherche de l'Institut du Cerveau et de la Moelle épinière (CRICM), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Coupé, Pierrick, and Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)

Subjects
[SDV.IB] Life Sciences [q-bio]/Bioengineering, [INFO.INFO-TI] Computer Science [cs]/Image Processing [eess.IV], [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, [INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing, [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], [SDV.IB]Life Sciences [q-bio]/Bioengineering, [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], [SDV.NEU] Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, [SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing
Abstract

International audience; Predicting Alzheimer's disease (AD) in individuals with some symp-toms of cognitive decline may have great influence on treatment choice and guide subject selection in trials on disease modifying drugs. Structural MRI has the potential of revealing early signs of neurodegeneration in the human brain and may thus aid in predicting and diagnosing AD. Surface-based cortical thickness measurements from T1-weighted MRI have demonstrated high sensi-tivity to cortical gray matter changes. In this study, we investigated the possibil-ity of using patterns of cortical thickness measurements for predicting AD in subjects with mild cognitive impairment (MCI). Specific patterns of atrophy were identified at four time periods before diagnosis of probable AD and fea-tures were selected as regions of interest within these patterns. The selected re-gions were used for cortical thickness measurements and applied in a classifier for testing the ability to predict AD at the four stages. The accuracy of the pre-diction improved as the time to conversion from MCI to AD decreased, from 70% at 3 years before the clinical criteria for AD was met, to 76% at 6 months before AD. These results show that prediction accuracies of conversion from MCI to AD can be improved by learning the atrophy patterns that are specific to the different stages of disease progression. This has the potential to guide the further development of imaging biomarkers in AD.

Published
2012

2. Multi-atlas labeling with population-specific template and non-local patch-based label fusion

Author

Vladimir Fonov, Pierrick Coupé, Simon Eskildsen, Manjón, José V., Louis Collins, D., Coupé, Pierrick, McConnell Brain Imaging Centre (MNI), Montreal Neurological Institute and Hospital, McGill University = Université McGill [Montréal, Canada]-McGill University = Université McGill [Montréal, Canada], Laboratoire Bordelais de Recherche en Informatique (LaBRI), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB), Aalborg University [Denmark] (AAU), ITACA, and Universitat Politècnica de València (UPV)

Subjects
[SDV.IB] Life Sciences [q-bio]/Bioengineering, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, [INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, [INFO.INFO-IM] Computer Science [cs]/Medical Imaging, [SDV.IB]Life Sciences [q-bio]/Bioengineering, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, [SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing
Abstract

International audience; We propose a new method combining a population-specific nonlinear template atlas approach with non-local patch-based structure segmentation for whole brain segmentation into individual structures. This way, we benefit from the efficient intensity-driven segmentation of the non-local means framework and from the global shape constraints imposed by the nonlinear template matching.

Published
2012

3. Validation of basal ganglia segmentation on a 3T MRI template

Author

Claire Haegelen, Nicolas Guizard, Pierrick Coupe, Florent Lalys, Pierre Jannin, Xavier Morandi, Louis Collins, D., Vision, Action et Gestion d'informations en Santé (VisAGeS), Institut National de la Santé et de la Recherche Médicale (INSERM)-Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE (IRISA-D5), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), McConnell Brain Imaging Centre (MNI), Montreal Neurological Institute and Hospital, McGill University = Université McGill [Montréal, Canada]-McGill University = Université McGill [Montréal, Canada], Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), and Lalys, Florent

Subjects
[SDV.IB] Life Sciences [q-bio]/Bioengineering, surgical procedures, operative, nervous system, [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], [SDV.IB]Life Sciences [q-bio]/Bioengineering, [SDV.NEU] Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], therapeutics, nervous system diseases
Abstract

Subthalamic nucleus (STN) deep brain stimulation (DBS) has been demonstrated as an efficient surgical treatment in patients with Parkinson's disease suffering from severe disabilities in their motor symptoms (tremor, akinesia, rigidity) [1]. Sometimes, patients with STN DBS have secondary neuropsychological and/or psychiatric effects because the STN has a small size and a functional subdivision into motor, associative and limbic parts [8]. Therefore, targeting is an important step of the neurosurgical procedure as the DBS could induce undesirable side-effects. To improve targeting, many authors created either anatomical atlases [2,4] or multimodal databases [5-7]. They demonstrated that multimodal databases improved targeting accuracy over anatomical atlases. We presented a validation automatic segmentation of the basal ganglia based on a manual segmentation of these structures completed on a 3T MRI-template. This is the first step to increase accuracy of the targeting of DBS in order to improve the clinical outcome of DBS procedures.

Published
2011

4. Anatomically Constrained Weak Classifier Fusion for Early Detection of Alzheimer's Disease

Author

Mawulawoé Komlagan, Vinh-Thong Ta, Xingyu Pan, Jean-Philippe Domenger, Louis Collins, D., Pierrick Coupe, Patch-based processing for medical and natural images (PICTURA), Laboratoire Bordelais de Recherche en Informatique (LaBRI), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB), McConnell Brain Imaging Centre (MNI), Montreal Neurological Institute and Hospital, McGill University = Université McGill [Montréal, Canada]-McGill University = Université McGill [Montréal, Canada], CPU, TRAIL, and Coupé, Pierrick

Subjects
[SDV.IB] Life Sciences [q-bio]/Bioengineering, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, [INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing, [INFO.INFO-IM] Computer Science [cs]/Medical Imaging, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, [SDV.IB]Life Sciences [q-bio]/Bioengineering, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, [SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing
Abstract

International audience; The early detection of Alzheimer's disease (AD) is a key step to accelerate the development of new therapies and to diminish the associated socio-economic burden. To address this challenging problem, several biomarkers based on MRI have been proposed. Although numer- ous efforts have been devoted to improve MRI-based feature quality or to increase machine learning methods accuracy, the current AD prog- nosis accuracy remains limited. In this paper, we propose to combine both high quality biomarkers and advanced learning method. Our ap- proach is based on a robust ensemble learning strategy using gray matter grading. The estimated weak classifiers are then fused into high infor- mative anatomical sub-ensembles. Through a sparse logistic regression, the most relevant anatomical sub-ensembles are selected, weighted and used as input to a global classifier. Validation on the full ADNI1 dataset demonstrates that the proposed method obtains competitive results of prediction of conversion to AD in the Mild Cognitive Impairment group with an accuracy of 75.6%.

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