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  • 1.
    Commowick, Olivier
    et al.
    Univ Rennes 1, CNRS, INSERM, VISAGES,U1228,UMR6074, Rennes, France..
    Istace, Audrey
    Hosp Civils Lyon, Lyon Sud Hosp, Dept Radiol, Lyon, France..
    Kain, Michael
    Univ Rennes 1, CNRS, INSERM, VISAGES,U1228,UMR6074, Rennes, France..
    Laurent, Baptiste
    Univ Brest, IBSAM, INSERM, LaTIM,UMR 1101, Brest, France..
    Leray, Florent
    Univ Rennes 1, CNRS, INSERM, VISAGES,U1228,UMR6074, Rennes, France..
    Simon, Mathieu
    Univ Rennes 1, CNRS, INSERM, VISAGES,U1228,UMR6074, Rennes, France..
    Pop, Sorina Camarasu
    Univ Claude Bernard Lyon 1, Univ Lyon,INSA Lyon, UJM St Etienne,CNRS,Inserm, CREATIS,UMR 5220,U1206, F-69621 Lyon, France..
    Girard, Pascal
    Univ Claude Bernard Lyon 1, Univ Lyon,INSA Lyon, UJM St Etienne,CNRS,Inserm, CREATIS,UMR 5220,U1206, F-69621 Lyon, France..
    Ameli, Roxana
    Hosp Civils Lyon, Lyon Sud Hosp, Dept Radiol, Lyon, France..
    Ferre, Jean-Christophe
    Univ Rennes 1, CNRS, INSERM, VISAGES,U1228,UMR6074, Rennes, France.;CHU Rennes, Dept Neuroradiol, F-35033 Rennes, France..
    Kerbrat, Anne
    Univ Rennes 1, CNRS, INSERM, VISAGES,U1228,UMR6074, Rennes, France.;CHU Rennes, Dept Neurol, F-35033 Rennes, France..
    Tourdias, Thomas
    CHU Bordeaux, Serv Neuroimagerie, Bordeaux, France..
    Cervenansky, Frederic
    Univ Claude Bernard Lyon 1, Univ Lyon,INSA Lyon, UJM St Etienne,CNRS,Inserm, CREATIS,UMR 5220,U1206, F-69621 Lyon, France..
    Glatard, Tristan
    Concordia Univ, Dept Comp Sci & Software Engn, Montreal, PQ, Canada..
    Beaumont, Jeremy
    Univ Rennes 1, CNRS, INSERM, VISAGES,U1228,UMR6074, Rennes, France..
    Doyle, Senan
    Pixyl Med, Grenoble, France..
    Forbes, Florence
    Pixyl Med, Grenoble, France.;Inria Grenoble Rhone Alpes, Grenoble, France..
    Knight, Jesse
    Univ Guelph, Sch Engn, Image Anal Med Lab, Guelph, ON, Canada..
    Khademi, April
    Ryerson Univ, Image Anal Med Lab IAMLAB, Toronto, ON, Canada..
    Mahbod, Amirreza
    KTH, School of Technology and Health (STH).
    Wang, Chunliang
    KTH, School of Technology and Health (STH).
    McKinley, Richard
    Univ Bern, Dept Diagnost & Intervent Neuroradiol, Inselspital, Bern, Switzerland..
    Wagner, Franca
    Univ Bern, Dept Diagnost & Intervent Neuroradiol, Inselspital, Bern, Switzerland..
    Muschelli, John
    Johns Hopkins Bloomberg Sch Publ Hlth, Baltimore, MD USA..
    Sweeney, Elizabeth
    Johns Hopkins Bloomberg Sch Publ Hlth, Baltimore, MD USA..
    Roura, Eloy
    Univ Girona, Res Inst Comp Vis & Robot VICOROB, Girona, Spain..
    Llado, Xavier
    Univ Girona, Res Inst Comp Vis & Robot VICOROB, Girona, Spain..
    Santos, Michel M.
    Univ Fed Pernambuco, Ctr Informat, Recife, Brazil..
    Santos, Wellington P.
    Univ Fed Pernambuco, Dept Engn Biomed, Recife, Brazil..
    Silva-Filho, Abel G.
    Univ Fed Pernambuco, Ctr Informat, Recife, Brazil..
    Tomas-Fernandez, Xavier
    Childrens Hosp, Dept Radiol, Computat Radiol Lab, 300 Longwood Ave, Boston, MA 02115 USA..
    Urien, Helene
    Univ Paris Saclay, LTCI, Telecom ParisTech, Paris, France..
    Bloch, Isabelle
    Univ Paris Saclay, LTCI, Telecom ParisTech, Paris, France..
    Valverde, Sergi
    Univ Girona, Res Inst Comp Vis & Robot VICOROB, Girona, Spain..
    Cabezas, Mariano
    Univ Girona, Res Inst Comp Vis & Robot VICOROB, Girona, Spain..
    Javier Vera-Olmos, Francisco
    Univ Rey Juan Carlos, Med Image Anal Lab, Madrid, Spain..
    Malpica, Norberto
    Univ Rey Juan Carlos, Med Image Anal Lab, Madrid, Spain..
    Guttmann, Charles
    Brigham & Womens Hosp, Dept Radiol, Ctr Neurol Imaging, 75 Francis St, Boston, MA 02115 USA..
    Vukusic, Sandra
    Hosp Civils Lyon, Lyon Sud Hosp, Dept Radiol, Lyon, France..
    Edan, Gilles
    Univ Rennes 1, CNRS, INSERM, VISAGES,U1228,UMR6074, Rennes, France.;CHU Rennes, Dept Neurol, F-35033 Rennes, France..
    Dojat, Michel
    Univ Grenoble Alpes, CHU Grenoble, GIN, Inserm,U1216, Grenoble, France..
    Styner, Martin
    Univ N Carolina, Dept Comp Sci, Chapel Hill, NC 27515 USA..
    Warfield, Simon K.
    Childrens Hosp, Dept Radiol, Computat Radiol Lab, 300 Longwood Ave, Boston, MA 02115 USA..
    Cotton, Francois
    Hosp Civils Lyon, Lyon Sud Hosp, Dept Radiol, Lyon, France..
    Barillot, Christian
    Univ Rennes 1, CNRS, INSERM, VISAGES,U1228,UMR6074, Rennes, France..
    Objective Evaluation of Multiple Sclerosis Lesion Segmentation using a Data Management and Processing Infrastructure2018In: Scientific Reports, ISSN 2045-2322, E-ISSN 2045-2322, Vol. 8, article id 13650Article in journal (Refereed)
    Abstract [en]

    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.

  • 2.
    Mahbod, Amirreza
    et al.
    KTH, School of Technology and Health (STH), Medical Engineering, Medical Image Processing and Visualization.
    Chowdhury, Manish
    KTH, School of Technology and Health (STH), Medical Engineering, Medical Image Processing and Visualization.
    Smedby, Örjan
    KTH, School of Technology and Health (STH), Medical Engineering, Medical Image Processing and Visualization.
    Wang, Chunliang
    KTH, School of Technology and Health (STH), Medical Engineering, Medical Image Processing and Visualization.
    Automatic brain segmentation using artificial neural networks with shape context2018In: Pattern Recognition Letters, ISSN 0167-8655, E-ISSN 1872-7344, Vol. 101, p. 74-79Article in journal (Refereed)
    Abstract [en]

    Segmenting brain tissue from MR scans is thought to be highly beneficial for brain abnormality diagnosis, prognosis monitoring, and treatment evaluation. Many automatic or semi-automatic methods have been proposed in the literature in order to reduce the requirement of user intervention, but the level of accuracy in most cases is still inferior to that of manual segmentation. We propose a new brain segmentation method that integrates volumetric shape models into a supervised artificial neural network (ANN) framework. This is done by running a preliminary level-set based statistical shape fitting process guided by the image intensity and then passing the signed distance maps of several key structures to the ANN as feature channels, in addition to the conventional spatial-based and intensity-based image features. The so-called shape context information is expected to help the ANN to learn local adaptive classification rules instead of applying universal rules directly on the local appearance features. The proposed method was tested on a public datasets available within the open MICCAI grand challenge (MRBrainS13). The obtained average Dice coefficient were 84.78%, 88.47%, 82.76%, 95.37% and 97.73% for gray matter (GM), white matter (WM), cerebrospinal fluid (CSF), brain (WM + GM) and intracranial volume respectively. Compared with other methods tested on the same dataset, the proposed method achieved competitive results with comparatively shorter training time.

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