Performance of a Deep Learning Algorithm for Automated Segmentation and Quantification of Traumatic Pelvic Hematomas on CT.

Dreizin, David; Zhou, Yuyin; Zhang, Yixiao; Tirada, Nikki; Yuille, Alan L

Dreizin, David; Zhou, Yuyin; Zhang, Yixiao; Tirada, Nikki; Yuille, Alan L.

Afiliación

Dreizin D; Department of Diagnostic Radiology and Nuclear Medicine & R Adams Cowley Shock Trauma Center, University of Maryland School of Medicine, Baltimore, MD, USA. daviddreizin@gmail.com.
Zhou Y; Computational Cognition, Vision, and Learning (CCVL), Johns Hopkins University, Baltimore, MD, USA.
Zhang Y; Computational Cognition, Vision, and Learning (CCVL), Johns Hopkins University, Baltimore, MD, USA.
Tirada N; Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, MD, USA.
Yuille AL; Computational Cognition, Vision, and Learning (CCVL), Johns Hopkins University, Baltimore, MD, USA.

J Digit Imaging ; 33(1): 243-251, 2020 02.

Article en En | MEDLINE | ID: mdl-31172331

Asunto(s)

Aprendizaje Profundo; Hematoma; Hematoma/diagnóstico por imagen; Humanos; Pelvis/diagnóstico por imagen; Tomografía Computarizada por Rayos X

Palabras clave

Artificial intelligence (AI); Computed tomography (CT); Computer-aided diagnosis (CAD); Convolutional neural network (CNN); Deep learning; Fully convolutional network (FCN); Hematoma volume; Pelvic fractures; Pelvic ring disruptions; Recurrent saliency transformation network (RSTN); Segmentation

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Aprendizaje Profundo / Hematoma Tipo de estudio: Prognostic_studies Límite: Humans Idioma: En Revista: J Digit Imaging Asunto de la revista: DIAGNOSTICO POR IMAGEM / INFORMATICA MEDICA / RADIOLOGIA Año: 2020 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Estados Unidos

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