Author: "Pixyl Medical [Grenoble]" - Searchworks@Jio Institute Digital Library Search Results

Your search keyword '"Pixyl Medical [Grenoble]"' showing total 14 results

Start Over Author "Pixyl Medical [Grenoble]"

14 results on '"Pixyl Medical [Grenoble]"'

1. Trustworthy clinical AI solutions: a unified review of uncertainty quantification in deep learning models for medical image analysis

Author: Lambert, Benjamin, Forbes, Florence, Tucholka, Alan, Doyle, Senan, Dehaene, Harmonie, Dojat, Michel, [GIN] Grenoble Institut des Neurosciences (GIN), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Grenoble Alpes (UGA), Pixyl Medical [Grenoble], Modèles statistiques bayésiens et des valeurs extrêmes pour données structurées et de grande dimension (STATIFY), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Jean Kuntzmann (LJK), Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA), and Lambert, Benjamin
Subjects: FOS: Computer and information sciences, Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Vision and Pattern Recognition (cs.CV), [SDV]Life Sciences [q-bio], Image and Video Processing (eess.IV), Computer Science - Computer Vision and Pattern Recognition, [INFO] Computer Science [cs], Electrical Engineering and Systems Science - Image and Video Processing, Machine Learning (cs.LG), [SDV] Life Sciences [q-bio], Artificial Intelligence (cs.AI), FOS: Electrical engineering, electronic engineering, information engineering, [INFO]Computer Science [cs]
Abstract: The full acceptance of Deep Learning (DL) models in the clinical field is rather low with respect to the quantity of high-performing solutions reported in the literature. Particularly, end users are reluctant to rely on the rough predictions of DL models. Uncertainty quantification methods have been proposed in the literature as a potential response to reduce the rough decision provided by the DL black box and thus increase the interpretability and the acceptability of the result by the final user. In this review, we propose an overview of the existing methods to quantify uncertainty associated to DL predictions. We focus on applications to medical image analysis, which present specific challenges due to the high dimensionality of images and their quality variability, as well as constraints associated to real-life clinical routine. We then discuss the evaluation protocols to validate the relevance of uncertainty estimates. Finally, we highlight the open challenges of uncertainty quantification in the medical field.
Published: 2022

2. Beyond Voxel Prediction Uncertainty: Identifying brain lesions you can trust

Author: Benjamin Lambert, Florence Forbes, Senan Doyle, Alan Tucholka, Michel Dojat, [GIN] Grenoble Institut des Neurosciences (GIN), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Grenoble Alpes (UGA), Pixyl Medical [Grenoble], Modèles statistiques bayésiens et des valeurs extrêmes pour données structurées et de grande dimension (STATIFY), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Jean Kuntzmann (LJK), Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), and Université Grenoble Alpes (UGA)
Subjects: FOS: Computer and information sciences, Computer Science - Machine Learning, Computer Vision and Pattern Recognition (cs.CV), Image and Video Processing (eess.IV), Computer Science - Computer Vision and Pattern Recognition, Electrical Engineering and Systems Science - Image and Video Processing, Machine Learning (cs.LG), Detection, Deep Learning, [MATH.MATH-ST]Mathematics [math]/Statistics [math.ST], Interpretabilty, FOS: Electrical engineering, electronic engineering, information engineering, Prediction, MS lesion
Abstract: Deep neural networks have become the gold-standard approach for the automated segmentation of 3D medical images. Their full acceptance by clinicians remains however hampered by the lack of intelligible uncertainty assessment of the provided results. Most approaches to quantify their uncertainty, such as the popular Monte Carlo dropout, restrict to some measure of uncertainty in prediction at the voxel level. In addition not to be clearly related to genuine medical uncertainty, this is not clinically satisfying as most objects of interest (e.g. brain lesions) are made of groups of voxels whose overall relevance may not simply reduce to the sum or mean of their individual uncertainties. In this work, we propose to go beyond voxel-wise assessment using an innovative Graph Neural Network approach, trained from the outputs of a Monte Carlo dropout model. This network allows the fusion of three estimators of voxel uncertainty: entropy, variance, and model's confidence; and can be applied to any lesion, regardless of its shape or size. We demonstrate the superiority of our approach for uncertainty estimate on a task of Multiple Sclerosis lesions segmentation., Comment: Accepted for presentation at the Workshop on Interpretability of Machine Intelligence in Medical Image Computing (iMIMIC) at MICCAI 2022
Published: 2022
Full Text: View/download PDF

3. Fast Uncertainty Quantification for Deep Learning-based MR Brain Segmentation

Author: Lambert, Benjamin, Forbes, Florence, Doyle, Senan, Tucholka, Alan, Dojat, Michel, Forbes, Florence, [GIN] Grenoble Institut des Neurosciences (GIN), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Grenoble Alpes (UGA), Pixyl Medical [Grenoble], Modèles statistiques bayésiens et des valeurs extrêmes pour données structurées et de grande dimension (STATIFY), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Jean Kuntzmann (LJK), Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA), and Laboratoire Jean Kuntzmann (LJK)
Subjects: [MATH.MATH-ST]Mathematics [math]/Statistics [math.ST], [MATH.MATH-ST] Mathematics [math]/Statistics [math.ST]
Abstract: International audience; Quantifying the uncertainty attached to Deep Learning models predictions can help their interpretation, and thus their acceptance in critical fields. Yet, current standard approaches rely on multi-steps approaches, increasing the inference time and memory cost. In clinical routine, the automated prediction has to integrate into the clinical consultation timeframe, raising the need for faster and more efficient uncertainty quantification methods. In this work, we propose a novel model, named as BEHT, and evaluate it on an automated segmentation task of White-Matter Hyperintensities from T2-weighted FLAIR MRI sequences of Multiple-Sclerosis (MS) patients. We demonstrate that this approach outputs predictive uncertainty much faster than the state-of-the-art Monte Carlo Dropout approach, with a similar-and even slightly better-accuracy. Interestingly, our approach distinguishes 2 distinct sources of uncertainties, namely aleatoric and epistemic uncertainties.
Published: 2022

4. The SPHERE infrared survey for exoplanets (SHINE)

Author: Langlois, M., Gratton, R., Lagrange, A.-M., Delorme, P., Boccaletti, A., Bonnefoy, M., Maire, A.-L., Mesa, D., Chauvin, G., Desidera, S., Vigan, A., Cheetham, A., Hagelberg, J., Feldt, M., Meyer, M., Rubini, P., Le Coroller, H., Cantalloube, F., Biller, B., Bonavita, M., Bhowmik, T., Brandner, W., Daemgen, S., D’orazi, V., Flasseur, O., Fontanive, C., Galicher, R., Girard, J., Janin-Potiron, P., Janson, M., Keppler, M., Kopytova, T., Lagadec, E., Lannier, J., Lazzoni, C., Ligi, R., Meunier, N., Perreti, A., Perrot, C., Rodet, L., Romero, C., Rouan, D., Samland, M., Salter, G., Sissa, E., Schmidt, T., Zurlo, A., Mouillet, D., Denis, L., Thiébaut, E., Milli, J., Wahhaj, Z., Beuzit, J.-L., Dominik, C., Henning, Th., Ménard, F., Müller, A., Schmid, H. M., Turatto, M., Udry, S., Abe, L., Antichi, J., Allard, F., Baruffolo, A., Baudoz, P., Baudrand, J., Bazzon, A., Blanchard, P., Carbillet, M., Carle, M., Cascone, E., Charton, J., Claudi, R., Costille, A., de Caprio, V., Delboulbé, A., Dohlen, K., Fantinel, D., Feautrier, P., Fusco, Thierry, Gigan, P., Giro, E., Gisler, D., Gluck, L., Gry, C., Hubin, N., Hugot, E., Jaquet, M., Kasper, M., Le Mignant, D., Llored, M., Madec, F., Magnard, Y., Martinez, P., Maurel, D., Messina, S., Möller-Nilsson, O., Mugnier, Laurent, Moulin, T., Origné, A., Pavlov, A., Perret, D., Petit, Cyril, Pragt, J., Puget, P., Rabou, P., Ramos, J., Rigal, F., Rochat, S., Roelfsema, R., Rousset, G., Roux, A., Salasnich, B., Sauvage, J.-F., Sevin, A., Soenke, C., Stadler, E., Suarez, M., Weber, L., Wildi, F., Rickman, E., Centre de Recherche Astrophysique de Lyon (CRAL), École normale supérieure - Lyon (ENS Lyon)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS), INAF - Osservatorio Astronomico di Padova (OAPD), Istituto Nazionale di Astrofisica (INAF), Institut de Planétologie et d'Astrophysique de Grenoble (IPAG), Centre National d'Études Spatiales [Toulouse] (CNES)-Observatoire des Sciences de l'Univers de Grenoble (OSUG ), Institut national des sciences de l'Univers (INSU - CNRS)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Grenoble Alpes (UGA)-Météo-France -Institut national des sciences de l'Univers (INSU - CNRS)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Grenoble Alpes (UGA)-Météo-France, Laboratoire d'études spatiales et d'instrumentation en astrophysique (LESIA (UMR_8109)), Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité), Max-Planck-Institut, Department of Biochemistry and Molecular Biology, Mayo Clinic, Geneva Observatory, Université de Genève = University of Geneva (UNIGE), Max Planck Institute for Radio Astronomy, University of Michigan [Ann Arbor], University of Michigan System, Institute for Particle Physics and Astrophysics [ETH Zürich] (IPA), Department of Physics [ETH Zürich] (D-PHYS), Eidgenössische Technische Hochschule - Swiss Federal Institute of Technology [Zürich] (ETH Zürich)- Eidgenössische Technische Hochschule - Swiss Federal Institute of Technology [Zürich] (ETH Zürich), Pixyl Medical [Grenoble], Max Planck Institute for Astronomy (MPIA), University of Edinburgh, Center for Space and Habitability (CSH), University of Bern, DOTA, ONERA, Université Paris Saclay [Châtillon], ONERA-Université Paris-Saclay, Laboratoire d'Astrophysique de Marseille (LAM), Aix Marseille Université (AMU)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS), Max-Planck-Institut für Astronomie (MPIA), Max-Planck-Gesellschaft, Joseph Louis LAGRANGE (LAGRANGE), Université Nice Sophia Antipolis (1965 - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire de la Côte d'Azur, COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Université Côte d'Azur (UCA)-Université Côte d'Azur (UCA)-Centre National de la Recherche Scientifique (CNRS), INAF - Osservatorio Astronomico di Brera (OAB), Centre d'Etudes pour le Développement des Territoires et l'Environnement (CEDETE), Université d'Orléans (UO), Laboratoire Hubert Curien [Saint Etienne] (LHC), Institut d'Optique Graduate School (IOGS)-Université Jean Monnet - Saint-Étienne (UJM)-Centre National de la Recherche Scientifique (CNRS), Laboratoire de Biomécanique et Mécanique des Chocs (LBMC UMR T9406 ), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Université Gustave Eiffel, Laboratoire des Sciences de l'Ingénieur pour l'Environnement - UMR 7356 (LaSIE), La Rochelle Université (ULR)-Centre National de la Recherche Scientifique (CNRS), Laboratoire d'études spatiales et d'instrumentation en astrophysique (LESIA), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université Paris Diderot - Paris 7 (UPD7)-Centre National de la Recherche Scientifique (CNRS), CHU Tenon [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU), Département Médico-Universitaire APPROCHES, Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU), CHU Pitié-Salpêtrière [AP-HP], Laboratoire Hippolyte Fizeau (FIZEAU), INAF - Osservatorio Astronomico di Capodimonte (OAC), Institute for Mathematics, Astrophysics and Particle Physics (IMAPP), Radboud University [Nijmegen], European Southern Observatory (ESO), Environnements et Paléoenvironnements OCéaniques (EPOC), Observatoire aquitain des sciences de l'univers (OASU), Université Sciences et Technologies - Bordeaux 1 (UB)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Sciences et Technologies - Bordeaux 1 (UB)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS), INAF - Osservatorio Astrofisico di Catania (OACT), NASA Goddard Space Flight Center (GSFC), Centre Hospitalier Henri Duffaut (Avignon), This work is supported by the French National Research Agency in the framework of the Investissements d’Avenir program (ANR-15-IDEX-02), through the funding of the 'Origin of Life' project of the Univ. Grenoble-Alpes. This work is jointly supported by the French National Programms (PNP and PNPS) and by the Action Spécifique Haute Résolution Angulaire (ASHRA) of CNRS/INSU co-funded by CNES.AV acknowledges funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No. 757561). A.-M.L. acknowledges funding from French National Research Agency (GIPSE project). C.P. acknowledges financial support from Fondecyt (grant 3190691) and financial support from the ICM (Iniciativa Científica Milenio) via the Núcleo Milenio de Formación Planetaria grant, from the Universidad de Valparaíso. T.H. acknowledges support from the European Research Council under the Horizon 2020 Framework Program via the ERC Advanced Grant Origins 83 24 28., ANR-15-IDEX-0002,UGA,IDEX UGA(2015), Sciences, EDP, and IDEX UGA - - UGA2015 - ANR-15-IDEX-0002 - IDEX - VALID
Subjects: planets and satellites: detection, methods: statistical, instrumentation: high angular resolution, planets and satellites: formation, techniques: image processing, methods: observational, [PHYS.ASTR]Physics [physics]/Astrophysics [astro-ph], [PHYS.ASTR] Physics [physics]/Astrophysics [astro-ph]
Abstract: International audience; Context. In recent decades, direct imaging has confirmed the existence of substellar companions (exoplanets or brown dwarfs) on wide orbits (>10 au) around their host stars. In striving to understand their formation and evolution mechanisms, in 2015 we initiated the SPHERE infrared survey for exoplanets (SHINE), a systematic direct imaging survey of young, nearby stars that is targeted at exploring their demographics.Aims. We aim to detect and characterize the population of giant planets and brown dwarfs beyond the snow line around young, nearby stars. Combined with the survey completeness, our observations offer the opportunity to constrain the statistical properties (occurrence, mass and orbital distributions, dependency on the stellar mass) of these young giant planets.Methods. In this study, we present the observing and data analysis strategy, the ranking process of the detected candidates, and the survey performances for a subsample of 150 stars that are representative of the full SHINE sample. Observations were conducted in a homogeneous way between February 2015 and February 2017 with the dedicated ground-based VLT/SPHERE instrument equipped with the IFS integral field spectrograph and the IRDIS dual-band imager, covering a spectral range between 0.9 and 2.3 μm. We used coronographic, angular, and spectral differential imaging techniques to achieve the best detection performances for this study, down to the planetary mass regime.Results. We processed, in a uniform manner, more than 300 SHINE observations and datasets to assess the survey typical sensitivity as a function of the host star and of the observing conditions. The median detection performance reached 5σ-contrasts of 13 mag at 200 mas and 14.2 mag at 800 mas with the IFS (YJ and YJH bands), and of 11.8 mag at 200 mas, 13.1 mag at 800 mas, and 15.8 mag at 3 as with IRDIS in H band, delivering one of the deepest sensitivity surveys thus far for young, nearby stars. A total of sixteen substellar companions were imaged in this first part of SHINE: seven brown dwarf companions and ten planetary-mass companions.These include two new discoveries, HIP 65426 b and HIP 64892 B, but not the planets around PDS70 that had not been originally selected for the SHINE core sample. A total of 1483 candidates were detected, mainly in the large field of view that characterizes IRDIS. The color-magnitude diagrams, low-resolution spectrum (when available with IFS), and follow-up observations enabled us to identify the nature (background contaminant or comoving companion) of about 86% of our subsample. The remaining cases are often connected to crowded-field follow-up observations that were missing. Finally, even though SHINE was not initially designed for disk searches, we imaged twelve circumstellar disks, including three new detections around the HIP 73145, HIP 86598, and HD 106906 systems.Conclusions. Nowadays, direct imaging provides a unique opportunity to probe the outer part of exoplanetary systems beyond 10 au to explore planetary architectures, as highlighted by the discoveries of: one new exoplanet, one new brown dwarf companion, and three new debris disks during this early phase of SHINE. It also offers the opportunity to explore and revisit the physical and orbital properties of these young, giant planets and brown dwarf companions (relative position, photometry, and low-resolution spectrum in near-infrared, predicted masses, and contrast in order to search for additional companions). Finally, these results highlight the importance of finalizing the SHINE systematic observation of about 500 young, nearby stars for a full exploration of their outer part to explore the demographics of young giant planets beyond 10 au and to identify the most interesting systems for the next generation of high-contrast imagers on very large and extremely large telescopes.
Published: 2021

5. In-depth direct imaging and spectroscopic characterization of the young Solar System analog HD 95086

Author: Desgrange, C., Chauvin, G., Christiaens, V., Cantalloube, F., Lefranc, L.-X., Le Coroller, H., Rubini, P., Otten, G. P. P. L., Beust, H., Bonavita, M., Delorme, P., Devinat, M., Gratton, R., Lagrange, A.-M., Langlois, M., Mesa, D., Milli, J., Szulágyi, J., Nowak, M., Rodet, L., Rojo, P., Petrus, S., Janson, M., Henning, T., Kral, Q., van Holstein, R. G., Ménard, F., Beuzit, J.-L., Biller, B., Boccaletti, A., Bonnefoy, M., Brown, S., Costille, A., Delboulbe, A., Desidera, S., D’Orazi, V., Feldt, M., Fusco, T., Galicher, R., Hagelberg, J., Lazzoni, C., Ligi, R., Maire, A.-L., Messina, S., Meyer, M., Potier, A., Ramos, J., Rouan, D., Schmidt, T., Vigan, A., Zurlo, A., Institut de Planétologie et d'Astrophysique de Grenoble (IPAG), Centre National d'Études Spatiales [Toulouse] (CNES)-Observatoire des Sciences de l'Univers de Grenoble (OSUG ), Institut national des sciences de l'Univers (INSU - CNRS)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Grenoble Alpes (UGA)-Météo-France -Institut national des sciences de l'Univers (INSU - CNRS)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Grenoble Alpes (UGA)-Météo-France, Laboratoire d'Astrophysique de Marseille (LAM), Aix Marseille Université (AMU)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS), École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL), Laboratoire d'études spatiales et d'instrumentation en astrophysique = Laboratory of Space Studies and Instrumentation in Astrophysics (LESIA), Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité), Centre de Recherche Astrophysique de Lyon (CRAL), École normale supérieure de Lyon (ENS de Lyon)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS), DOTA, ONERA, Université Paris Saclay [Palaiseau], ONERA-Université Paris-Saclay, Observatoire de la Côte d'Azur (OCA), Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS), Departamento de Astronomía, Universidad de Chile = University of Chile [Santiago] (UCHILE), Max-Planck-Institut für Astronomie (MPIA), Max-Planck-Gesellschaft, Pixyl Medical [Grenoble], INAF - Osservatorio Astronomico di Padova (OAPD), Istituto Nazionale di Astrofisica (INAF), École normale supérieure - Lyon (ENS Lyon)-Université Claude Bernard Lyon 1 (UCBL), Department of Biochemistry and Molecular Biology, Mayo Clinic, Institute of Astronomy [ETH Zürich], Department of Physics [ETH Zürich] (D-PHYS), Eidgenössische Technische Hochschule - Swiss Federal Institute of Technology [Zürich] (ETH Zürich)- Eidgenössische Technische Hochschule - Swiss Federal Institute of Technology [Zürich] (ETH Zürich), Laboratoire d'études spatiales et d'instrumentation en astrophysique (LESIA (UMR_8109)), Cornell Center for Astrophysics and Planetary Science (CCAPS), Cornell University [New York], and Observatoire de Paris
Subjects: Earth and Planetary Astrophysics (astro-ph.EP), [SDU.ASTR]Sciences of the Universe [physics]/Astrophysics [astro-ph], instrumentation: high angular resolution, [SDU]Sciences of the Universe [physics], Space and Planetary Science, FOS: Physical sciences, Astronomy and Astrophysics, stars: individual: HD95086, methods: observational, [PHYS.ASTR]Physics [physics]/Astrophysics [astro-ph], instrumentation: adaptive optics, planetary systems, Astrophysics - Earth and Planetary Astrophysics
Abstract: Context. HD 95086 is a young nearby Solar System analog hosting a giant exoplanet orbiting at 57 au from the star between an inner and outer debris belt. The existence of additional planets has been suggested as the mechanism that maintains the broad cavity between the two belts. Aims. We present a dedicated monitoring of HD 95086 with the VLT/SPHERE instrument to refine the orbital and atmospheric properties of HD 95086 b, and to search for additional planets in this system. Methods. SPHERE observations, spread over ten epochs from 2015 to 2019 and including five new datasets, were used. Combined with archival observations, from VLT/NaCo (2012-2013) and Gemini/GPI (2013-2016), the extended set of astrometric measurements allowed us to refine the orbital properties of HD 95086 b. We also investigated the spectral properties and the presence of a circumplanetary disk around HD 95086 b by using the special fitting tool exploring the diversity of several atmospheric models. In addition, we improved our detection limits in order to search for a putative planet c via the K-Stacker algorithm. Results. We extracted for the first time the JH low-resolution spectrum of HD 95086 b by stacking the six best epochs, and confirm its very red spectral energy distribution. Combined with additional datasets from GPI and NaCo, our analysis indicates that this very red color can be explained by the presence of a circumplanetary disk around planet b, with a range of high-temperature solutions (1400-1600 K) and significant extinction (Av > 10 mag), or by a super-solar metallicity atmosphere with lower temperatures (800-1300 K), and small to medium amount of extinction (Av < 10 mag). We do not find any robust candidates for planet c, but give updated constraints on its potential mass and location., 29 pages, 20 figures, A&A, accepted
Published: 2022

6. A SOPHIE RV search for giant planets around young nearby stars (YNS)

Author: Grandjean, A., Lagrange, A.-M., Meunier, N., Rubini, P., Desidera, S., Galland, F., Borgniet, S., Zicher, N., Messina, S., Chauvin, G., Sterzik, M., Pantoja, B., Institut de Planétologie et d'Astrophysique de Grenoble (IPAG), Centre National d'Études Spatiales [Toulouse] (CNES)-Observatoire des Sciences de l'Univers de Grenoble (OSUG ), Institut national des sciences de l'Univers (INSU - CNRS)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Grenoble Alpes (UGA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Grenoble Alpes (UGA), Laboratoire d'études spatiales et d'instrumentation en astrophysique (LESIA (UMR_8109)), Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université de Paris (UP), Pixyl Medical [Grenoble], INAF - Osservatorio Astronomico di Padova (OAPD), Istituto Nazionale di Astrofisica (INAF), Centre d'Immunologie de Marseille - Luminy (CIML), Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU), Oxford Astrophysics, University of Oxford [Oxford], INAF - Osservatorio Astrofisico di Catania (OACT), European Southern Observatory (ESO), Universidad de Chile = University of Chile [Santiago] (UCHILE), Institut national des sciences de l'Univers (INSU - CNRS)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Grenoble Alpes (UGA)-Météo-France -Institut national des sciences de l'Univers (INSU - CNRS)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Grenoble Alpes (UGA)-Météo-France, Laboratoire d'études spatiales et d'instrumentation en astrophysique = Laboratory of Space Studies and Instrumentation in Astrophysics (LESIA), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité), Aix Marseille Université (AMU)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), University of Oxford, and Gestionnaire, HAL Sorbonne Université 5
Subjects: [SDU.ASTR]Sciences of the Universe [physics]/Astrophysics [astro-ph], general, activity -binaries, variables, Astrophysics::Solar and Stellar Astrophysics, spectroscopic -planetary systems -starspotsstars, Astrophysics::Cosmology and Extragalactic Astrophysics, Astrophysics::Earth and Planetary Astrophysics, radial velocities -stars, [SDU.ASTR] Sciences of the Universe [physics]/Astrophysics [astro-ph], techniques, Astrophysics::Galaxy Astrophysics
Abstract: International audience; Context. The search of close (a 5 au) giant planet (GP) companions with radial velocity (RV) around young stars and the estimate of their occurrence rates is important to constrain the migration timescales. Furthermore, this search will allow the giant planet occurrence rates to be computed at all separations via the combination with direct imaging techniques. The RV search around young stars is a challenge as they are generally faster rotators than older stars of similar spectral types and they exhibit signatures of magnetic activity (spots) or pulsation in their RV time series. Specific analyses are necessary to characterize, and possibly correct for, this activity. Aims. Our aim is to search for planets around young nearby stars and to estimate the GP occurrence rates for periods up to 1000 days. Methods. We used the SOPHIE spectrograph on the 1.93 m telescope at the Haute-Provence Observatory to observe 63 A − M young (
Published: 2021

7. Automatic quantification of brain lesion volume from post-trauma MR Images

Author: Jean-François Payen, Alexandre Krainik, Kremer S, Christophe Maggia, Irène Troprès, Senan Doyle, Schmitt E, Thomas Mistral, Michel Dojat, Emmanuel L. Barbier, Alan Tucholka, P Roca, Adrian Kastler, Galanaud D, Florence Forbes, [GIN] Grenoble Institut des Neurosciences (GIN), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Grenoble Alpes (UGA), Pixyl Medical [Grenoble], Laboratoire Jean Kuntzmann (LJK), Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA), Modèles statistiques bayésiens et des valeurs extrêmes pour données structurées et de grande dimension (STATIFY), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Jean Kuntzmann (LJK), Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), IRMaGe (IRMaGe), CHU Grenoble-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA), CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU), Centre Hospitalier Régional Universitaire de Nancy (CHRU Nancy), Les Hôptaux universitaires de Strasbourg (HUS), and CHU Strasbourg
Subjects: business.industry, Traumatic brain injury, [SDV]Life Sciences [q-bio], Statistical difference, medicine.disease, Mr imaging, 030218 nuclear medicine & medical imaging, Lesion, 03 medical and health sciences, 0302 clinical medicine, Text mining, [MATH.MATH-ST]Mathematics [math]/Statistics [math.ST], Medicine, Brain lesions, Mr images, medicine.symptom, business, Nuclear medicine, 030217 neurology & neurosurgery, Volume (compression)
Abstract: ObjectivesThe determination of the volume of brain lesions after trauma is challenging. Manual delineation is observer-dependent and time-consuming which inhibits the practice in clinical routine. We propose and evaluate an automated atlas-based quantification procedure (AQP) based on the detection of abnormal mean diffusivity (MD) values computed from diffusion-weighted MR images.MethodsWe measured the performance of AQP versus manual delineation consensus by independent raters in two series of experiments: i) realistic trauma phantoms (n=5) where abnormal MD values were assigned to healthy brain images according to the intensity, form and location of lesion observed in real TBI cases; ii) severe TBI patients (n=12 patients) who underwent MR imaging within 10 days after injury.ResultsIn realistic trauma phantoms, no statistical difference in Dice similarity coefficient, precision and brain lesion volumes was found between AQP, the rater consensus and the ground truth lesion delineations. Similar findings were obtained when comparing AQP and manual annotations for TBI patients. The intra-class correlation coefficient between AQP and manual delineation was 0.70 in realistic phantoms and 0.92 in TBI patients. The volume of brain lesions detected in TBI patients was 59 ml (19-84 ml) (median; 25-75th centiles).Conclusionsour results indicate that an automatic quantification procedure could accurately determine with accuracy the volume of brain lesions after trauma. This presents an opportunity to support the individualized management of severe TBI patients.Key pointsThe management of patients with severe traumatic brain injury is complex, and access to objective quantitative information lesion volumes can support clinical decision-making.An automated delineation procedure was developed to determine the nature and volume of brain lesions post-trauma.This procedure was based on diffusion weighted MR-imaging to quantify the volume of vasogenic and cellular edema from realistic phantoms and patients with severe traumatic brain injury.Nature and quantification of the brain lesions volume compared favorably with manual delineation of brain lesions by a panel of experts.
Published: 2021

8. Breaking the clinico-radiological paradox in multiple sclerosis using machine learning

Author: Attyé, Arnaud, Cackowski, Stenzel, Tucholka, Alan, Roca, Pauline, Rubini, Pascal, Verclytte, Sebastien, Colas, Lucie, Ding, Juliette, Budzik, Jean-François, Renard, Felix, Barbier, Emmanuel L, Casey, Romain, Vukusic, Sandra, Cotton, François, [GIN] Grenoble Institut des Neurosciences (GIN), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Grenoble Alpes (UGA), Pixyl Medical [Grenoble], Université catholique de Lille (UCL), Laboratoire d'Informatique de Grenoble (LIG), Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon, Observatoire Français de la Sclérose En Plaques [Lyon] (OFSEP), Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé (CREATIS), Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM), and ANR-19-P3IA-0003,MIAI,MIAI @ Grenoble Alpes(2019)
Subjects: [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], [SDV]Life Sciences [q-bio], education
Abstract: International audience; MRI is central to the study of white matter lesions in multiple sclerosis (MS). To date, the distribution of MS lesions, as evaluated on FLAIR imaging, has not been linked to patients’ disability prediction. Based on an international data challenge with 1500 MS patients and ground truth 2-year Expanded Disability Status Scale (EDSS), we have proposed an adaptive machine learning framework to predict the clinical disability. Here, we report the encouraging finding that our algorithm predicts the 2-year EDSS score with an accuracy estimated to 81%, only based on a single initial FLAIR sequence, added to sex and gender information.
Published: 2020

9. Objective Evaluation of Multiple Sclerosis Lesion Segmentation using a Data Management and Processing Infrastructure

Author: Franca Wagner, Sorina Camarasu-Pop, Hélène Urien, Isabelle Bloch, Xavier Tomas-Fernandez, Jeremy Beaumont, John Muschelli, Chunliang Wang, Baptiste Laurent, Charles R.G. Guttmann, Tristan Glatard, Michael Kain, Mathieu Simon, Michel Dojat, April Khademi, Martin Styner, Pascal Girard, Thomas Tourdias, Michel M. dos Santos, Wellington Pinheiro dos Santos, Sergi Valverde, Richard McKinley, Abel G. Silva-Filho, Jesse Knight, Eloy Roura, Sandra Vukusic, Francisco Javier Vera-Olmos, François Cotton, Xavier Lladó, Elizabeth M. Sweeney, Senan Doyle, Anne Kerbrat, Olivier Commowick, Mariano Cabezas, Florent Leray, Amirreza Mahbod, Norberto Malpica, Gilles Edan, Roxana Ameli, Christian Barillot, Frederic Cervenansky, Florence Forbes, Simon K. Warfield, Audrey Istace, Jean-Christophe Ferré, 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 ) -Université de Bretagne Sud ( UBS ) -École normale supérieure - Rennes ( ENS Rennes ) -Institut National de Recherche en Informatique et en Automatique ( Inria ) -CentraleSupélec-Centre National de la Recherche Scientifique ( CNRS ) -IMT Atlantique Bretagne-Pays de la Loire ( IMT Atlantique ) -Université de Rennes 1 ( UR1 ), Université de Rennes ( UNIV-RENNES ) -Université de Rennes ( UNIV-RENNES ) -Institut National des Sciences Appliquées - Rennes ( INSA Rennes ) -Université de Bretagne Sud ( UBS ) -École normale supérieure - Rennes ( ENS Rennes ) -Institut National de Recherche en Informatique et en Automatique ( Inria ) -CentraleSupélec-Centre National de la Recherche Scientifique ( CNRS ) -IMT Atlantique Bretagne-Pays de la Loire ( IMT Atlantique ) -Institut de Recherche en Informatique et Systèmes Aléatoires ( IRISA ), Université de Rennes ( UNIV-RENNES ) -Université de Rennes ( UNIV-RENNES ) -Institut National des Sciences Appliquées - Rennes ( INSA Rennes ) -Université de Bretagne Sud ( UBS ) -École normale supérieure - Rennes ( ENS Rennes ) -CentraleSupélec-Centre National de la Recherche Scientifique ( CNRS ) -IMT Atlantique Bretagne-Pays de la Loire ( IMT Atlantique ), Centre Hospitalier Lyon Sud [CHU - HCL] ( CHLS ), Hospices Civils de Lyon ( HCL ), Laboratoire de Traitement de l'Information Medicale ( LaTIM ), Université européenne de Bretagne ( UEB ) -Télécom Bretagne-Centre Hospitalier Régional Universitaire de Brest ( CHRU Brest ) -Université de Brest ( UBO ) -Institut National de la Santé et de la Recherche Médicale ( INSERM ) -Institut Mines-Télécom [Paris], Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé ( CREATIS ), Université Claude Bernard Lyon 1 ( UCBL ), Université de Lyon-Université de Lyon-Institut National des Sciences Appliquées de Lyon ( INSA Lyon ), Université de Lyon-Institut National des Sciences Appliquées ( INSA ) -Institut National des Sciences Appliquées ( INSA ) -Hospices Civils de Lyon ( HCL ) -Université Jean Monnet [Saint-Étienne] ( UJM ) -Institut National de la Santé et de la Recherche Médicale ( INSERM ) -Centre National de la Recherche Scientifique ( CNRS ), Service de neurochirurgie [Rennes], Université de Rennes ( UNIV-RENNES ) -Université de Rennes ( UNIV-RENNES ) -Hôpital Pontchaillou-CHU Pontchaillou [Rennes], Service de neurologie [Rennes], Université de Rennes ( UNIV-RENNES ) -Université de Rennes ( UNIV-RENNES ), Neuroinflammation: imagerie et thérapie de la sclérose en plaques, Université Bordeaux Segalen - Bordeaux 2-Institut National de la Santé et de la Recherche Médicale ( INSERM ), Concordia University [Montreal], Pixyl Medical [Grenoble], Modelling and Inference of Complex and Structured Stochastic Systems ( MISTIS ), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique ( Inria ) -Institut National de Recherche en Informatique et en Automatique ( Inria ) -Laboratoire Jean Kuntzmann ( LJK ), Université Pierre Mendès France - Grenoble 2 ( UPMF ) -Université Joseph Fourier - Grenoble 1 ( UJF ) -Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique ( CNRS ) -Université Grenoble Alpes ( UGA ) -Université Pierre Mendès France - Grenoble 2 ( UPMF ) -Université Joseph Fourier - Grenoble 1 ( UJF ) -Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique ( CNRS ) -Université Grenoble Alpes ( UGA ) -Institut National Polytechnique de Grenoble ( INPG ), University of Guelph, Department of Electrical and Computer Engineering [TORONTO] ( ECE ), University of Toronto, Royal Institute of Technology [Stockholm] ( KTH ), University of Bern, Johns Hopkins Bloomberg School of Public Health [Baltimore, MD, USA], Visio per computador I robotica ( VICOROB ), Universitat de Girona ( UdG ), Universidade Federal de Pernambuco [Recife] ( UFPE ), Computational Radiology Laboratory [Boston] ( CRL ), Brigham and Women's Hospital [Boston]-Children's Hospital, Laboratoire Traitement et Communication de l'Information ( LTCI ), Télécom ParisTech-Institut Mines-Télécom [Paris]-Université Paris-Saclay, Universidad Rey Juan Carlos [Madrid] ( URJC ), Brigham and Women's Hospital [Boston], Service de Neurologie, CHU Pontchaillou, Rennes, Grenoble Institut des Neurosciences ( GIN ), Université Joseph Fourier - Grenoble 1 ( UJF ) -CHU Grenoble-Institut National de la Santé et de la Recherche Médicale ( INSERM ), Computer Science ( North Carolina State University ), North Carolina State University [Raleigh] ( NCSU ), 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 (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)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-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)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), Centre Hospitalier Lyon Sud [CHU - HCL] (CHLS), Hospices Civils de Lyon (HCL), Laboratoire de Traitement de l'Information Medicale (LaTIM), Université européenne de Bretagne - European University of Brittany (UEB)-Université de Brest (UBO)-Télécom Bretagne-Institut Mines-Télécom [Paris] (IMT)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre Hospitalier Régional Universitaire de Brest (CHRU Brest), Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé (CREATIS), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Département de Radiologie [CHU de Rennes], Université de Rennes (UR), CHU Pontchaillou [Rennes], CHU Bordeaux [Bordeaux], Modelling and Inference of Complex and Structured Stochastic Systems (MISTIS ), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Laboratoire Jean Kuntzmann (LJK ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Ryerson University [Toronto], Royal Institute of Technology [Stockholm] (KTH ), Johns Hopkins Bloomberg School of Public Health [Baltimore], Johns Hopkins University (JHU), Research institute of Computer Vision and Robotics [Girona] (VICOROB), Universitat de Girona (UdG), Universidade Federal de Pernambuco [Recife] (UFPE), Computational Radiology Laboratory [Boston] (CRL), Brigham and Women's Hospital [Boston]-Boston Children's Hospital, Laboratoire Traitement et Communication de l'Information (LTCI), Télécom ParisTech-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS), Universidad Rey Juan Carlos [Madrid] (URJC), Service de Neurologie [Lyon], CHU Lyon, [GIN] Grenoble Institut des Neurosciences (GIN), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Grenoble Alpes (UGA), University of North Carolina [Chapel Hill] (UNC), University of North Carolina System (UNC), This work was partly funded by France Life Imaging (grant ANR-11-INBS-0006 from the French 'Investissements d’Avenir' program) for funding and sponsoring the challenge. This work has also been partly supported by a grant (OFSEP) provided by the French State and handled by the 'Agence nationale de la recherche', within the framework of the 'Investissements d’Avenir' program, under the reference ANR-10-COHO-002. We also thank the French national cohort OFSEP (a French 'Investissements d’Avenir' program), and particularly the imaging group inside this cohort consortium for their constant support, fruitful discussions on the challenge and providing the MR images., ANR-11-INBS-0006,FLI,France Life Imaging(2011), ANR-10-COHO-0002,OFSEP,Observatoire Français de la Sclérose en Plaques(2010), 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)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-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)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Université européenne de Bretagne - European University of Brittany (UEB)-Télécom Bretagne-Centre Hospitalier Régional Universitaire de Brest (CHRU Brest)-Université de Brest (UBO)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut Mines-Télécom [Paris] (IMT), Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES), Modelling and Inference of Complex and Structured Stochastic Systems (MISTIS), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Jean Kuntzmann (LJK), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut National Polytechnique de Grenoble (INPG), Boston Children's Hospital-Brigham and Women's Hospital [Boston], Grenoble Institut des Neurosciences (GIN), Université Joseph Fourier - Grenoble 1 (UJF)-Centre Hospitalier Universitaire [Grenoble] (CHU)-Institut National de la Santé et de la Recherche Médicale (INSERM), ANR-11-INBS-0006/11-INBS-0006,FLI,France In vivo Imaging(2011), ANR-10-COHO-002-01/10-COHO-0002,OFSEP,Observatoire Français de la Sclérose en Plaques(2010), Université de Bretagne Sud (UBS)-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 Rennes (UNIV-RENNES)-Institut National de Recherche en Informatique et en Automatique (Inria)-École normale supérieure - Rennes (ENS Rennes)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-CentraleSupélec-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Bretagne Sud (UBS)-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 Rennes (UNIV-RENNES)-École normale supérieure - Rennes (ENS Rennes)-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)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique)-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)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Brest (UBO)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre Hospitalier Régional Universitaire de Brest (CHRU Brest)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique)-Institut Brestois Santé Agro Matière (IBSAM), Université de Brest (UBO), Images et Modèles, Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Hospices Civils de Lyon (HCL)-Université Jean Monnet [Saint-Étienne] (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Hospices Civils de Lyon (HCL)-Université Jean Monnet [Saint-Étienne] (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Hôpital Pontchaillou-CHU Pontchaillou [Rennes], Université Bordeaux Segalen - Bordeaux 2-Institut National de la Santé et de la Recherche Médicale (INSERM), Service Informatique et développements, Department of Electrical and Computer Engineering [University of Toronto] (ECE), Télécom ParisTech-Institut Mines-Télécom [Paris] (IMT), Service de Neurologie [CHU Rennes], Université Joseph Fourier - Grenoble 1 (UJF)-CHU Grenoble-Institut National de la Santé et de la Recherche Médicale (INSERM), Computer Science (North Carolina State University), North Carolina State University [Raleigh] (NC State), University of North Carolina System (UNC)-University of North Carolina System (UNC), RMN et optique : De la mesure au biomarqueur, Edan, Gilles, Infrastructures - France Life Imaging - - FLI2011 - ANR-11-INBS-0006 - INBS - VALID, Cohortes - Observatoire Français de la Sclérose en Plaques - - OFSEP2010 - ANR-10-COHO-0002 - COHO - VALID, and Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Hospices Civils de Lyon (HCL)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)
Subjects: Male, Computer science, Data management, MESH: Parenchymal Tissue, lcsh:Medicine, Esclerosi múltiple, computer.software_genre, 030218 nuclear medicine & medical imaging, MESH: Magnetic Resonance Imaging, Machine Learning, 0302 clinical medicine, open science, Image Processing, Computer-Assisted, Segmentation, [ SDV.IB ] Life Sciences [q-bio]/Bioengineering, Image processing -- Digital techniques, lcsh:Science, 610 Medicine & health, Multiple sclerosis lesion, image segmentation, MESH: Machine Learning, Magnetic Resonance Imaging, MESH: Image Processing, Computer-Assisted, Random forest, performance evaluation, MESH: Neural Networks, Computer, [SDV.IB]Life Sciences [q-bio]/Bioengineering, Female, [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], Objective evaluation, Algorithms, MESH: Algorithms, Machine learning, Article, Multiple sclerosis, 03 medical and health sciences, Imatges -- Processament -- Tècniques digitals, distributed computing, Image Interpretation, Computer-Assisted, Humans, [SDV.NEU] Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], Parenchymal Tissue, Retrospective Studies, MESH: Humans, business.industry, Deep learning, Imatge -- Segmentació, lcsh:R, MESH: Retrospective Studies, MESH: Multiple Sclerosis, MESH: Male, Imaging segmentation, computing infrastructure, [ SDV.NEU ] Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], lcsh:Q, Neural Networks, Computer, Artificial intelligence, business, computer, MESH: Female, MESH: Image Interpretation, Computer-Assisted, 030217 neurology & neurosurgery
Abstract: We present a study of multiple sclerosis segmentation algorithms conducted at the international MICCAI 2016 challenge. This challenge was operated using a new open-science computing infrastructure. This allowed for the automatic and independent evaluation of a large range of algorithms in a fair and completely automatic manner. This computing infrastructure was used to evaluate thirteen methods of MS lesions segmentation, exploring a broad range of state-of-theart algorithms, against a high-quality database of 53 MS cases coming from four centers following a common definition of the acquisition protocol. Each case was annotated manually by an unprecedented number of seven different experts. Results of the challenge highlighted that automatic algorithms, including the recent machine learning methods (random forests, deep learning, …), are still trailing human expertise on both detection and delineation criteria. In addition, we demonstrate that computing a statistically robust consensus of the algorithms performs closer to human expertise on one score (segmentation) although still trailing on detection scores This work was partly funded by France Life Imaging (grant ANR-11-INBS-0006 from the French “Investissements d’Avenir” program) for funding and sponsoring the challenge. This work has also been partly supported by a grant (OFSEP) provided by the French State and handled by the “Agence nationale de la recherche”, within the framework of the “Investissements d’Avenir” program, under the reference ANR-10-COHO-002. We also thank the French national cohort OFSEP (a French “Investissements d’Avenir” program), and particularly the imaging group inside this cohort consortium for their constant support, fruitful discussions on the challenge and providing the MR images
Published: 2018

10. Traumatic Brain Lesion Quantification based on Mean Diffusivity Changes

Author: Emmanuelle Schmitt, Alexandre Krainik, Jean-François Payen, Thomas Mistral, Michel Dojat, Florence Forbes, Stéphane Kremer, Senan Doyle, Damien Galanaud, Irène Troprès, Emmanuel L. Barbier, Christophe Maggia, [GIN] Grenoble Institut des Neurosciences (GIN), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Grenoble Alpes (UGA), Université Grenoble Alpes - UFR Médecine (UGA UFRM), Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Pixyl Medical [Grenoble], Modelling and Inference of Complex and Structured Stochastic Systems (MISTIS ), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Laboratoire Jean Kuntzmann (LJK ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Service de Neuroradiologie [CHU Pitié-Salpêtrière], CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU), Centre Hospitalier Régional Universitaire de Nancy (CHRU Nancy), Service de radiologie [Strasbourg], CHU Strasbourg-Hôpital de Hautepierre [Strasbourg], RMN biomédicale : de la cellule à l'homme (RBCH), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-CHU Grenoble-DIR CENTRALE DU SSA-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU), IRMaGe (IRMaGe ), CHU Grenoble-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), and Forbes, Florence
Subjects: Traumatic brain injury, business.industry, Computer science, Brain tissue, medicine.disease, Thermal diffusivity, 030218 nuclear medicine & medical imaging, 03 medical and health sciences, 0302 clinical medicine, [MATH.MATH-ST]Mathematics [math]/Statistics [math.ST], medicine, Brain lesions, Nuclear medicine, business, Mr diffusion, [MATH.MATH-ST] Mathematics [math]/Statistics [math.ST], Brain trauma, 030217 neurology & neurosurgery, Automated method
Abstract: International audience; We report the evaluation of an automated method for quantification of brain tissue damage, caused by a severe traumatic brain injury, using mean diffusivity computed from MR diffusion images. Our automatic results obtained on realistic phantoms and real patient images 10 days post-event provided by nine different centers were coherent with four expert manually identified lesions. For realistic phantoms automated method scores were equal to 0.77, 0.77 and 0.83 for Dice, Precision and Sensibility respectively compared to 0.78, 0.72 and 0.86 for the experts. The inter correlation class (ICC) was 0.79. For 7/9 real cases 0.57, 0.50 and 0.70 were respectively obtained for automated method compared to 0.60, 0.52 and 0.78 for experts with ICC=0.71. Additionally, we detail the quality control module used to pool data from various image provider centers. This study clearly demonstrates the validity of the proposed automated method to eventually compute in a multi-centre project, the lesional load following brain trauma based on MD changes.
Published: 2018

11. Minimum Information about a Biosynthetic Gene cluster

Author: Patrick Caffrey, Renzo Kottmann, Eriko Takano, Sean Doyle, Axel A. Brakhage, Matthew Cummings, Juan Pablo Gomez-Escribano, Yvonne Mast, Ryan F. Seipke, Rob Lavigne, Markus Nett, Hans-Wilhelm Nützmann, Jan Claesen, David H. Sherman, Daniel Petras, Pablo Cruz-Morales, Carl J. Balibar, Anne Osbourn, Oscar P. Kuipers, Leonilde M. Moreira, Xinyu Liu, Marcia S. Osburne, Bohdan Ostash, David P. Fewer, Changsheng Zhang, Pelin Yilmaz, Mohamed S. Donia, Anja Greule, Hyun Uk Kim, Nicholas J. Tobias, Frank Oliver Glöckner, Christoph Geiger, Chia Y. Lee, William H. Gerwick, Philipp Wiemann, Bertolt Gust, Susan E. Jensen, Wilfred A. van der Donk, Jan Kormanec, Ben Shen, Christopher M. Thomas, Jason Micklefield, Srikanth Duddela, R. Cameron Coates, René De Mot, Anthony S. Haines, Neha Garg, Guohui Pan, Roderich D. Süssmuth, Hyung Jin Kwon, Jonathan D. Walton, Lena Gerwick, Jörn Piel, Monika Ehling-Schulz, Zhenhua Tian, Jonathan L. Klassen, Xiaohui Yan, Emily A. Monroe, Yunchang Xie, Russell J. Cox, Keishi Ishida, Grace Yim, Stefano Donadio, Nadine Ziemert, Yuta Tsunematsu, Matthew L. Hillwig, Miroslav Petricek, Sylvie Lautru, Tilmann Weber, Andrew W. Truman, Rainer Breitling, Peter Kötter, Nikos C. Kyrpides, Stephanie Düsterhus, Christian Hertweck, Hideaki Oikawa, Sean F. Brady, Christopher T. Walsh, Adam C. Jones, Marcus A. Moore, Bradley S. Moore, Barrie Wilkinson, Simone M. Mantovani, Nathan A. Moss, Elizabeth E. Wyckoff, Emily P. Balskus, Kapil Tahlan, Fengan Yu, Monica Höfte, Jos M. Raaijmakers, Taifo Mahmud, Yit-Heng Chooi, Yi Tang, Andreas Bechthold, Douglas A. Mitchell, Joanne M. Willey, Helge B. Bode, John B. Biggins, Margherita Sosio, Yi-Qiang Cheng, Carmen Méndez, Leonard Kaysser, Joleen Masschelein, Daniel Krug, Federico Rosconi, Marnix H. Medema, Kaarina Sivonen, Tomohisa Kuzuyama, Mikko Metsä-Ketelä, Esther K. Schmitt, Carsten Kegler, Andriy Luzhetskyy, Gilles P. van Wezel, Bai Linquan, Kai Blin, Jens Nielsen, Bertrand Aigle, Amrita Pati, Harald Gross, Muriel Viaud, Pieter C. Dorrestein, Carla S. Jones, Michael A. Fischbach, Shelley M. Payne, Zhe Rui, Gerard D. Wright, Wen Liu, Alexey V. Melnik, Barry Scott, Brett A. Neilan, Nancy P. Keller, Rainer Borriss, Katrin Jungmann, Michalis Hadjithomas, Evi Stegmann, Daniel J. Edwards, F. Jerry Reen, Alexander Kristian Apel, Wolfgang Wohlleben, Michael J. Smanski, Leonard Katz, Fergal O'Gara, Eric J. N. Helfrich, Sergey B. Zotchev, Olivier Ploux, Arnold J. M. Driessen, Rolf Müller, Jean-Luc Pernodet, K. D. Entian, José A. Salas, Irene de Bruijn, Francisco Barona-Gómez, Jianhua Ju, Jon Clardy, Molecular Microbiology, Molecular Genetics, Jacobs University [Bremen], Microbial genomics and bioinformatics research group, Max Planck Institute for Marine Microbiology, Max-Planck-Gesellschaft-Max-Planck-Gesellschaft, Max-Planck-Gesellschaft, Atmospheric Chemistry Observations and Modeling Laboratory (ACOML), National Center for Atmospheric Research [Boulder] (NCAR), Department of Food and Environmental Sciences, Helsingin yliopisto = Helsingfors universitet = University of Helsinki, Collaborative Mass Spectrometry Innovation Center, University of California [San Diego] (UC San Diego), University of California (UC)-University of California (UC), Heilongjiang Institute of Science and Technology, Eidgenössische Technische Hochschule - Swiss Federal Institute of Technology [Zürich] (ETH Zürich), Merck Stiftungsprofessur fûr Molekulare Biotechnologie Fachbereich Biowissenscharten, Goethe-University Frankfurt am Main, Department of Opto-Mechatronics Engineering and Cogno-Mechatronics Engineering, Pusan National University, University of Liverpool, College of Computer Science and Technology [Zhejiang] (Zhejiang University), University of Florida [Gainesville] (UF), School of Management, University of Science and Technology of China [Hefei] (USTC), State Key Laboratory of Nuclear Physics and Technology (SKL-NPT), Peking University [Beijing], Massachusetts Institute of Technology (MIT), Memorial Sloane Kettering Cancer Center [New York], South China Sea Institute of Oceanology, Chinese Academy of Sciences [Beijing] (CAS), Dynamique des Génomes et Adaptation Microbienne (DynAMic), Institut National de la Recherche Agronomique (INRA)-Université de Lorraine (UL), Institut für Biologie [Berlin] (IFB), Humboldt University Of Berlin, School of Biomolecular and Biomedical Science and Centre for Synthesis and Chemical Biology, University College Dublin [Dublin] (UCD), Parallélisme, Réseaux, Systèmes, Modélisation (PRISM), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Centre National de la Recherche Scientifique (CNRS), Skaggs School of Pharmacy and Pharmaceutical Sciences [San Diego], Grenoble Institut des Neurosciences (GIN), Université Joseph Fourier - Grenoble 1 (UJF)-Institut National de la Santé et de la Recherche Médicale (INSERM), Pixyl Medical [Grenoble], Integrated Optical MicroSystems (IOMS), University of Twente-MESA+ Institute for Nanotechnology, 7Lehrstuhl für Mikrobielle Ökologie, Department für Grundlagen der Biowissenschaften, Technische Universität Munchen - Université Technique de Munich [Munich, Allemagne] (TUM), Service Néphrologie Pédiatrique, CHU Strasbourg-Hôpital de Hautepierre [Strasbourg], Advanced Resources and Risk Technology, Laboratory of Phytopathology (K.C., H.S., B.A., M.H.), Universiteit Gent = Ghent University (UGENT), Trifork Aarhus C, Aalborg University [Denmark] (AAU), Centers for Disease Control and Prevention [Atlanta] (CDC), Centers for Disease Control and Prevention, Groupe d'Etude de la Matière Condensée (GEMAC), Groningen Biomolecular Sciences and Biotechnology Institute (GBB), University of Groningen [Groningen], DOE Joint Genome Institute [Walnut Creek], Microbiologie Moléculaire des Actinomycètes (ACTINO), Département Microbiologie (Dpt Microbio), Institut de Biologie Intégrative de la Cellule (I2BC), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Institut de Biologie Intégrative de la Cellule (I2BC), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Institut de génétique et microbiologie [Orsay] (IGM), Université Paris-Sud - Paris 11 (UP11)-Centre National de la Recherche Scientifique (CNRS), Laboratory of Gene Technology, Catholic University of Leuven - Katholieke Universiteit Leuven (KU Leuven), Joint Center for Structural Genomics (JCSG), Stanford University, Centre européen de recherche et d'enseignement des géosciences de l'environnement (CEREGE), Institut de Recherche pour le Développement (IRD)-Institut National de la Recherche Agronomique (INRA)-Aix Marseille Université (AMU)-Collège de France (CdF (institution))-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS), Centre de Recherches et d'Applications Pédagogiques en Langues (CRAPEL), Université Nancy 2, Space Sciences Laboratory [Berkeley] (SSL), University of California [Berkeley] (UC Berkeley), Polytechnic Institute of Leiria, NMR Laboratory, Université de Mons, Université de Mons (UMons), School of Biomedical Science, Curtin University [Perth], Planning and Transport Research Centre (PATREC)-Planning and Transport Research Centre (PATREC), BIOMERIT Research Centre, School of Microbiology, University College Cork (UCC), Department of Engineering Science, University of Oxford, Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Laboratoire Charles Friedel, Ecole Nationale Supérieure de Chimie de Paris - Chimie ParisTech-PSL (ENSCP), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS), Laboratory of Phytopathology, Wageningen University and Research [Wageningen] (WUR), Department of Microbial Ecology, Netherlands Institute of Ecology, Department of Animal Production, Universidad de Córdoba = University of Córdoba [Córdoba], IMV Technologies, Gulliver (UMR 7083), Ecole Superieure de Physique et de Chimie Industrielles de la Ville de Paris (ESPCI Paris), Institut für Chemie, Technical University of Berlin / Technische Universität Berlin (TU), Lipides - Nutrition - Cancer (U866) (LNC), Université de Bourgogne (UB)-Institut National de la Santé et de la Recherche Médicale (INSERM)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Ecole Nationale Supérieure de Biologie Appliquée à la Nutrition et à l'Alimentation de Dijon (ENSBANA), Centre de Recherche Paul Pascal (CRPP), Université de Bordeaux (UB)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS), DEPARTMENT OF CHEMISTRY, Durham University, Molekulare Ökologie, Joint Attosecond Science Laboratory, University of Ottawa and National Research Council, Department of Mechanical and Aerospace Engineering [Univ California Davis] (MAE - UC Davis), University of California [Davis] (UC Davis), University of Helsinki, University of California-University of California, Université de Lorraine (UL)-Institut National de la Recherche Agronomique (INRA), Humboldt-Universität zu Berlin, University of Twente [Netherlands]-MESA+ Institute for Nanotechnology, Technische Universität München [München] (TUM), Universiteit Gent = Ghent University [Belgium] (UGENT), Department of Biosystems, KU Leuven, Aix Marseille Université (AMU)-Institut national des sciences de l'Univers (INSU - CNRS)-Collège de France (CdF (institution))-Institut de Recherche pour le Développement (IRD)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Recherche Agronomique (INRA), University of California [Berkeley], NMR and Molecular Imaging Laboratory [Mons], University of Mons [Belgium] (UMONS), University of Oxford [Oxford], Centre National de la Recherche Scientifique (CNRS)-Ecole Nationale Supérieure de Chimie de Paris - Chimie ParisTech-PSL (ENSCP), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut de Chimie du CNRS (INC), Universidad de Córdoba [Cordoba], Technische Universität Berlin (TU), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Bourgogne (UB)-Ecole Nationale Supérieure de Biologie Appliquée à la Nutrition et à l'Alimentation de Dijon (ENSBANA)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement, Department of Mechanical and Aerospace Engineering [Davis], Eidgenössische Technische Hochschule - Swiss Federal Institute of Technology in Zürich [Zürich] (ETH Zürich), University of Florida [Gainesville], Institut für Biologie, Humboldt Universität zu Berlin, Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Grenoble-Université Joseph Fourier - Grenoble 1 (UJF), Ghent University [Belgium] (UGENT), Université Paris-Sud - Paris 11 (UP11)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Centre National de la Recherche Scientifique (CNRS)-Université Paris-Saclay-Université Paris-Sud - Paris 11 (UP11)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Centre National de la Recherche Scientifique (CNRS)-Université Paris-Saclay-Institut de Biologie Intégrative de la Cellule (I2BC), Université Paris-Sud - Paris 11 (UP11)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Centre National de la Recherche Scientifique (CNRS)-Université Paris-Saclay-Université Paris-Sud - Paris 11 (UP11)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Centre National de la Recherche Scientifique (CNRS)-Université Paris-Saclay, Stanford University [Stanford], Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Collège de France (CdF)-Institut national des sciences de l'Univers (INSU - CNRS)-Aix Marseille Université (AMU)-Institut National de la Recherche Agronomique (INRA), NMR and Molecular Imaging Laboratory, Université Paris-Sud - Paris 11 (UP11)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Centre National de la Recherche Scientifique (CNRS)-Université Paris-Saclay, Ecole Nationale Supérieure de Chimie de Paris- Chimie ParisTech-PSL (ENSCP)-Centre National de la Recherche Scientifique (CNRS), Wageningen University and Research Centre [Wageningen] (WUR), Gulliver, ESPCI ParisTech-Centre National de la Recherche Scientifique (CNRS), Technische Universität Berlin (TUB), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS), Max Planck Society (GERMANY), Max Planck Society (GERMANY)-Max Planck Society (GERMANY), Laboratoire Leprince-Ringuet ( LLR ), Institut National de Physique Nucléaire et de Physique des Particules du CNRS ( IN2P3 ) -École polytechnique ( X ) -Centre National de la Recherche Scientifique ( CNRS ), Atmospheric Chemistry Observations and Modeling Laboratory ( ACOML ), National Center for Atmospheric Research [Boulder] ( NCAR ), University of California [San Diego] ( UC San Diego ), Eidgenössische Technische Hochschule [Zürich] ( ETH Zürich ), University of Science and Technology of China [Hefei] ( USTC ), State Key Laboratory of Nuclear Physics and Technology ( SKL-NPT ), Massachusetts Institute of Technology ( MIT ), Memorial Sloan Kettering Cancer Center ( MSKCC ), Shanghai Ocean University, Dynamique des Génomes et Adaptation Microbienne ( DynAMic ), Institut National de la Recherche Agronomique ( INRA ) -Université de Lorraine ( UL ), University College Dublin [Dublin] ( UCD ), Parallélisme, Réseaux, Systèmes, Modélisation ( PRISM ), Université de Versailles Saint-Quentin-en-Yvelines ( UVSQ ) -Centre National de la Recherche Scientifique ( CNRS ), Grenoble Institut des Neurosciences ( GIN ), Institut National de la Santé et de la Recherche Médicale ( INSERM ) -CHU Grenoble-Université Joseph Fourier - Grenoble 1 ( UJF ), Integrated Optical MicroSystems ( IOMS ), Technische Universität München [München] ( TUM ), Ghent University [Belgium] ( UGENT ), Aalborg University [Denmark] ( AAU ), Centers for Disease Control and Prevention [Atlanta] ( CDC ), Groupe d'Etude de la Matière Condensée ( GEMAC ), Groningen Biomolecular Sciences and Biotechnology Institute ( GBB ), Microbiologie Moléculaire des Actinomycètes ( ACTINO ), Département Microbiologie ( Dpt Microbio ), Institut de Biologie Intégrative de la Cellule ( I2BC ), Université Paris-Sud - Paris 11 ( UP11 ) -Commissariat à l'énergie atomique et aux énergies alternatives ( CEA ) -Université Paris-Saclay-Centre National de la Recherche Scientifique ( CNRS ) -Université Paris-Sud - Paris 11 ( UP11 ) -Commissariat à l'énergie atomique et aux énergies alternatives ( CEA ) -Université Paris-Saclay-Centre National de la Recherche Scientifique ( CNRS ) -Institut de Biologie Intégrative de la Cellule ( I2BC ), Université Paris-Sud - Paris 11 ( UP11 ) -Commissariat à l'énergie atomique et aux énergies alternatives ( CEA ) -Université Paris-Saclay-Centre National de la Recherche Scientifique ( CNRS ) -Université Paris-Sud - Paris 11 ( UP11 ) -Commissariat à l'énergie atomique et aux énergies alternatives ( CEA ) -Université Paris-Saclay-Centre National de la Recherche Scientifique ( CNRS ), Institut de génétique et microbiologie [Orsay] ( IGM ), Université Paris-Sud - Paris 11 ( UP11 ) -Centre National de la Recherche Scientifique ( CNRS ), Joint Center for Structural Genomics ( JCSG ), Centre européen de recherche et d'enseignement de géosciences de l'environnement ( CEREGE ), Centre National de la Recherche Scientifique ( CNRS ) -Institut de Recherche pour le Développement ( IRD ) -Aix Marseille Université ( AMU ) -Collège de France ( CdF ) -Institut National de la Recherche Agronomique ( INRA ) -Institut national des sciences de l'Univers ( INSU - CNRS ), Centre de Recherches et d'Applications Pédagogiques en Langues ( CRAPEL ), Space Sciences Laboratory [Berkeley] ( SSL ), Université de Mons ( UMons ), Planning and Transport Research Centre ( PATREC ) -Planning and Transport Research Centre ( PATREC ), University College Cork ( UCC ), Université Paris-Sud - Paris 11 ( UP11 ) -Commissariat à l'énergie atomique et aux énergies alternatives ( CEA ) -Université Paris-Saclay-Centre National de la Recherche Scientifique ( CNRS ), Ecole Nationale Supérieure de Chimie de Paris- Chimie ParisTech-PSL ( ENSCP ) -Centre National de la Recherche Scientifique ( CNRS ), Wageningen University and Research Centre [Wageningen] ( WUR ), ESPCI ParisTech, Technische Universität Berlin ( TUB ), Lipides - Nutrition - Cancer (U866) ( LNC ), Université de Bourgogne ( UB ) -Institut National de la Santé et de la Recherche Médicale ( INSERM ) -AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Ecole Nationale Supérieure de Biologie Appliquée à la Nutrition et à l'Alimentation de Dijon ( ENSBANA ), Centre de recherche Paul Pascal, CNRS, Université de Bordeaux ( UPR8641 ), Centre de Recherche Paul Pascal, CNRS, Université de Bordeaux, University Durham, University of California [Davis] ( UC Davis ), Université Paris-Sud - Paris 11 (UP11)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Université Paris-Sud - Paris 11 (UP11)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Institut de Biologie Intégrative de la Cellule (I2BC), Université Paris-Sud - Paris 11 (UP11)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Université Paris-Sud - Paris 11 (UP11)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Université Paris-Sud - Paris 11 (UP11)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), [GIN] Grenoble Institut des Neurosciences (GIN), Helmholtz Institute for Pharmaceutical Research Saarland (HIPS), and Saarland University, Building A4.1, 66123 Saarbruecken, Germany.
Subjects: MESH : Protein Biosynthesis, protein synthesis, Operon, MESH : Polysaccharides, International Cooperation, MESH: Plants, plant, Review, MESH: Terpenes, gene cluster, polyketide, data base, genetic database, Gene cluster, acyltransferase, Databases, Genetic, MESH : Metagenome, MESH : Genetic Markers, genetics, terpene, GeneralLiterature_REFERENCE(e.g.,dictionaries,encyclopedias,glossaries), ComputingMilieux_MISCELLANEOUS, MESH : Peptides, MESH: Peptides, biology, fungus, nonribosomal peptide synthesis, Plants, bacterium, peptide, priority journal, MESH: Protein Biosynthesis, Multigene Family, MESH : Terpenes, MESH: Computational Biology, Genetic Markers, MESH: Terminology as Topic, Bioinformatics, MESH : Multigene Family, biological activity, Article, metagenome, Alkaloids, Manchester Institute of Biotechnology, Terminology as Topic, Bioinformatica, MESH : Bacteria, Peptide Biosynthesis, MESH : Databases, Genetic, Molecular Biology, MESH : Fungi, MESH: Polyketides, standardization, secondary metabolism, [ SDV ] Life Sciences [q-bio], Bacteria, ta1182, Computational Biology, MESH : Terminology as Topic, operon, Laboratorium voor Phytopathologie, MESH: International Cooperation, gene function, Metagenomics, polysaccharide, Laboratory of Phytopathology, chemical structure, Metagenome, MESH: Multigene Family, EPS, biosynthesis, Peptides, MESH : Computational Biology, MESH : International Cooperation, [SDV]Life Sciences [q-bio], MESH: Genetic Markers, information, MESH : Alkaloids, Synthetic biology, MESH: Peptide Biosynthesis, Nucleic Acid-Independent, database, MESH: Databases, Genetic, Genetics, MESH : Polyketides, MESH : Peptide Biosynthesis, Nucleic Acid-Independent, ddc:540, standards, Peptide Biosynthesis, Nucleic Acid-Independent, ComputingMethodologies_DOCUMENTANDTEXTPROCESSING, nomenclature, genetic marker, alkaloid derivative, MESH: Fungi, Biology, MESH : Plants, peptide derivative, Polyketide, MESH: Alkaloids, Polysaccharides, ddc:570, Life Science, 14. Life underwater, Secondary metabolism, enzyme specificity, Gene, nonhuman, Terpenes, Fungi, nucleotide sequence, Cell Biology, MESH: Metagenome, ResearchInstitutes_Networks_Beacons/manchester_institute_of_biotechnology, alkaloid, MESH: Bacteria, MESH: Polysaccharides, 13. Climate action, Polyketides, Protein Biosynthesis, synthetic biology, metabolism
Abstract: M.H.M. was supported by a Rubicon fellowship of the Netherlands Organization for Scientific Research (NWO;Rubicon 825.13.001). The work of R.K. was supported by the European Union’s Seventh Framework Programme(Joint Call OCEAN.2011–2: Marine microbial diversity—new insights into marine ecosystems functioning and its biotechnological potential) under the grant agreement no.287589 (Micro B3). M.C. was supported by a Biotechnology and Biological Sciences Research Council (BBSRC)studentship (BB/J014478/1). The GSC is supported by funding from the Natural Environment Research Council(UK), the National Institute for Energy Ethics and Society(NIEeS; UK), the Gordon and Betty Moore Foundation,the National Science Foundation (NSF; US) and the US Department of Energy. The Manchester Synthetic Biology Research Centre, SYNBIOCHEM, is supported by BBSRC/Engineering and Physical Sciences Research Council(EPSRC) grant BB/M017702/1, Medema, M.H., Kottmann, R., Yilmaz, P., Cummings, M., Biggins, J.B., Blin, K., De Bruijn, I., Chooi, Y.H., Claesen, J., Coates, R.C., Cruz-Morales, P., Duddela, S., Düsterhus, S., Edwards, D.J., Fewer, D.P., Garg, N., Geiger, C., Gomez-Escribano, J.P., Greule, A., Hadjithomas, M., Haines, A.S., Helfrich, E.J.N., Hillwig, M.L., Ishida, K., Jones, A.C., Jones, C.S., Jungmann, K., Kegler, C., Kim, H.U., Kötter, P., Krug, D., Masschelein, J., Melnik, A.V., Mantovani, S.M., Monroe, E.A., Moore, M., Moss, N., Nützmann, H.-W., Pan, G., Pati, A., Petras, D., Reen, F.J., Rosconi, F., Rui, Z., Tian, Z., Tobias, N.J., Tsunematsu, Y., Wiemann, P., Wyckoff, E., Yan, X., Yim, G., Yu, F., Xie, Y., Aigle, B., Apel, A.K., Balibar, C.J., Balskus, E.P., Barona-Gómez, F., Bechthold, A., Bode, H.B., Borriss, R., Brady, S.F., Brakhage, A.A., Caffrey, P., Cheng, Y.Q., Clardy, J., Cox, R.J., De Mot, R., Donadio, S., Donia, M.S., Van Der Donk, W.A., Dorrestein, P.C., Doyle, S., Driessen, A.J.M., Ehling-Schulz, M., Entian, K.-D., Fischbach, M.A., Gerwick, L., Gerwick, W.H., Gross, H., Gust, B., Hertweck, C., Höfte, M., Jensen, S.E., Ju, J., Katz, L., Kaysser, L., Klassen, J.L., Keller, N.P., Kormanec, J., Kuipers, O.P., Kuzuyama, T., Kyrpides, N.C., Kwon, H.-J., Lautru, S., Lavigne, R., Lee, C.Y., Linquan, B., Liu, X., Liu, W., Luzhetskyy, A., Mahmud, T., Mast, Y., Méndez, C., Metsä-Ketelä, M., Micklefield, J., Mitchell, D.A., Moore, B.S., Moreira, L.M., Müller, R., Neilan, B.A., Nett, M., Nielsen, J., O'Gara, F., Oikawa, H., Osbourn, A., Osburne, M.S., Ostash, B., Payne, S.M., Pernodet, J.-L., Petricek, M., Piel, J., Ploux, O., Raaijmakers, J.M., Salas, J.A., Schmitt, E.K., Scott, B., Seipke, R.F., Shen, B., Sherman, D.H., Sivonen, K., Smanski, M.J., Sosio, M., Stegmann, E., Süssmuth, R.D., Tahlan, K., Thomas, C.M., Tang, Y., Truman, A.W., Viaud, M., Walton, J.D., Walsh, C.T., Weber, T., Van Wezel, G.P., Wilkinson, B., Willey, J.M., Wohlleben, W., Wright, G.D., Ziemert, N., Zhang, C., Zotchev, S.B., Breitling, R., Takano, E., Glöckner, F.O.
Published: 2015

12. Assessment of Tissue Injury in Severe Brain Trauma

Author: Corentin Berthet, Christophe Maggia, Irène Troprès, Senan Doyle, Yann Teyssier, Olivier Heck, Michel Dojat, Florence Forbes, Jean François Payen, Lionel Velly, Grenoble Institut des Neurosciences (GIN), Université Joseph Fourier - Grenoble 1 (UJF)-Institut National de la Santé et de la Recherche Médicale (INSERM), Pixyl Medical [Grenoble], Modelling and Inference of Complex and Structured Stochastic Systems (MISTIS ), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Laboratoire Jean Kuntzmann (LJK ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Neuro-imagerie fonctionnelle et métabolique (ANTE-INSERM U836, équipe 5), Université Joseph Fourier - Grenoble 1 (UJF)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut National de la Santé et de la Recherche Médicale (INSERM), Forbes, Florence, [GIN] Grenoble Institut des Neurosciences (GIN), and Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])
Subjects: medicine.medical_specialty, Traumatic brain injury, business.industry, [SDV]Life Sciences [q-bio], Hemorrhagic lesion, medicine.disease, computer.software_genre, 3. Good health, Lesion load, [SDV] Life Sciences [q-bio], Voxel, [MATH.MATH-ST]Mathematics [math]/Statistics [math.ST], Tissue damage, medicine, Effective diffusion coefficient, Radiology, business, Brain trauma, computer, [MATH.MATH-ST] Mathematics [math]/Statistics [math.ST], Diffusion MRI
Abstract: International audience; We report our methodological developments to investigate, in a multi-center study using mean diffusivity, the tissue damage caused by a severe traumatic brain injury (GSC
Published: 2016

13. Automatic multiple sclerosis lesion segmentation with P-LOCUS

Author: Doyle, Senan, Forbes, Florence, Dojat, Michel, Pixyl Medical [Grenoble], Modelling and Inference of Complex and Structured Stochastic Systems (MISTIS ), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Laboratoire Jean Kuntzmann (LJK ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Grenoble Institut des Neurosciences (GIN), Université Joseph Fourier - Grenoble 1 (UJF)-Institut National de la Santé et de la Recherche Médicale (INSERM), Duchange, Nathalie, [GIN] Grenoble Institut des Neurosciences (GIN), and Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])
Subjects: [SDV.IB] Life Sciences [q-bio]/Bioengineering, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, [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]
Abstract: International audience; P-LOCUS provides automatic quantitative neuroimaging bio-marker extraction tools to aid diagnosis, prognosis and follow-up in multiple sclerosis studies. The software performs accurate and precise seg-mentation of multiple sclerosis lesions in a multi-stage process. In the first step, a weighted Gaussian tissue model is used to perform a robust segmentation. The algorithm avails of complementary information from multiple MR sequences, and includes additional estimated weight variables to account for the relative importance of each voxel. These estimated weights are used to define candidate lesion voxels that are not well described by a normal tissue model. In the second step, the candidate le-sion regions are used to populate the weighted Gaussian model and guide convergence to an optimal solution. The segmentation is unsupervised, removing the need for a training dataset, and providing independence from specific scanner type and MRI scanner protocol.
Published: 2016

14. Objective Evaluation of Multiple Sclerosis Lesion Segmentation using a Data Management and Processing Infrastructure.

Author: Commowick O, Istace A, Kain M, Laurent B, Leray F, Simon M, Pop SC, Girard P, Améli R, Ferré JC, Kerbrat A, Tourdias T, Cervenansky F, Glatard T, Beaumont J, Doyle S, Forbes F, Knight J, Khademi A, Mahbod A, Wang C, McKinley R, Wagner F, Muschelli J, Sweeney E, Roura E, Lladó X, Santos MM, Santos WP, Silva-Filho AG, Tomas-Fernandez X, Urien H, Bloch I, Valverde S, Cabezas M, Vera-Olmos FJ, Malpica N, Guttmann C, Vukusic S, Edan G, Dojat M, Styner M, Warfield SK, Cotton F, and Barillot C
Subjects: Female, Humans, Image Interpretation, Computer-Assisted methods, Image Processing, Computer-Assisted methods, Machine Learning, Male, Multiple Sclerosis pathology, Neural Networks, Computer, Parenchymal Tissue pathology, Retrospective Studies, Algorithms, Magnetic Resonance Imaging methods, Multiple Sclerosis diagnosis, Multiple Sclerosis diagnostic imaging, Parenchymal Tissue diagnostic imaging
Abstract: We present a study of multiple sclerosis segmentation algorithms conducted at the international MICCAI 2016 challenge. This challenge was operated using a new open-science computing infrastructure. This allowed for the automatic and independent evaluation of a large range of algorithms in a fair and completely automatic manner. This computing infrastructure was used to evaluate thirteen methods of MS lesions segmentation, exploring a broad range of state-of-theart algorithms, against a high-quality database of 53 MS cases coming from four centers following a common definition of the acquisition protocol. Each case was annotated manually by an unprecedented number of seven different experts. Results of the challenge highlighted that automatic algorithms, including the recent machine learning methods (random forests, deep learning, …), are still trailing human expertise on both detection and delineation criteria. In addition, we demonstrate that computing a statistically robust consensus of the algorithms performs closer to human expertise on one score (segmentation) although still trailing on detection scores.
Published: 2018
Full Text: View/download PDF

Catalog

Books, media, physical & digital resources

See catalog results

Searchworks

Select search scope, currently: Articles

Catalog

books, media & more in Jio Institute collections

Articles

journal articles & other e-resources

Refine your results

14 results on '"Pixyl Medical [Grenoble]"'

1. Trustworthy clinical AI solutions: a unified review of uncertainty quantification in deep learning models for medical image analysis

2. Beyond Voxel Prediction Uncertainty: Identifying brain lesions you can trust

3. Fast Uncertainty Quantification for Deep Learning-based MR Brain Segmentation

4. The SPHERE infrared survey for exoplanets (SHINE)

5. In-depth direct imaging and spectroscopic characterization of the young Solar System analog HD 95086

6. A SOPHIE RV search for giant planets around young nearby stars (YNS)

7. Automatic quantification of brain lesion volume from post-trauma MR Images

8. Breaking the clinico-radiological paradox in multiple sclerosis using machine learning

9. Objective Evaluation of Multiple Sclerosis Lesion Segmentation using a Data Management and Processing Infrastructure

10. Traumatic Brain Lesion Quantification based on Mean Diffusivity Changes

11. Minimum Information about a Biosynthetic Gene cluster

12. Assessment of Tissue Injury in Severe Brain Trauma

13. Automatic multiple sclerosis lesion segmentation with P-LOCUS

14. Objective Evaluation of Multiple Sclerosis Lesion Segmentation using a Data Management and Processing Infrastructure.

Catalog

Searchworks

Select search scope, currently: Articles Catalog books, media & more in Jio Institute collections Articles journal articles & other e-resources

Search

Search Constraints

Refine your results

Search Limiters

Topic

Publication Year Range

Language

Publication Type

Journal

Database

Publisher

14 results on '"Pixyl Medical [Grenoble]"'

Search Results

Catalog

Select search scope, currently: Articles

Catalog

books, media & more in Jio Institute collections

Articles

journal articles & other e-resources