A comparison of supervised machine learning algorithms and feature vectors for MS lesion segmentation using multimodal structural MRI.

Sweeney, Elizabeth M; Vogelstein, Joshua T; Cuzzocreo, Jennifer L; Calabresi, Peter A; Reich, Daniel S; Crainiceanu, Ciprian M; Shinohara, Russell T

Sweeney, Elizabeth M; Vogelstein, Joshua T; Cuzzocreo, Jennifer L; Calabresi, Peter A; Reich, Daniel S; Crainiceanu, Ciprian M; Shinohara, Russell T.

Afiliación

Sweeney EM; Department of Biostatistics, The Johns Hopkins University, Baltimore, Maryland, United States of America; Translational Neuroradiology Unit, Neuroimmunology Branch, National Institute of Neurological Disease and Stroke, National Institute of Health, Bethesda, Maryland, United States of America.
Vogelstein JT; Department of Statistical Science, Duke University, Durham, North Carolina, United States of America; Center for the Developing Brain, Child Mind Institute, New York, New York, United States of America.
Cuzzocreo JL; Department of Radiology, The Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America.
Calabresi PA; Department of Neurology, The Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America.
Reich DS; Department of Biostatistics, The Johns Hopkins University, Baltimore, Maryland, United States of America; Translational Neuroradiology Unit, Neuroimmunology Branch, National Institute of Neurological Disease and Stroke, National Institute of Health, Bethesda, Maryland, United States of America; Depa
Crainiceanu CM; Department of Biostatistics, The Johns Hopkins University, Baltimore, Maryland, United States of America.
Shinohara RT; Department of Biostatistics and Epidemiology, Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.

PLoS One ; 9(4): e95753, 2014.

Article en En | MEDLINE | ID: mdl-24781953

Asunto(s)

Algoritmos; Inteligencia Artificial; Imagen por Resonancia Magnética/métodos; Esclerosis Múltiple/patología; Humanos

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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Algoritmos / Inteligencia Artificial / Imagen por Resonancia Magnética / Esclerosis Múltiple Tipo de estudio: Guideline / Prognostic_studies Límite: Humans Idioma: En Revista: PLoS One Asunto de la revista: CIENCIA / MEDICINA Año: 2014 Tipo del documento: Article País de afiliación: Estados Unidos

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