3D vertebrae labeling in spine CT: an accurate, memory-efficient (Ortho2D) framework.

Huang, Y; Uneri, A; Jones, C K; Zhang, X; Ketcha, M D; Aygun, N; Helm, P A; Siewerdsen, J H

Huang, Y; Uneri, A; Jones, C K; Zhang, X; Ketcha, M D; Aygun, N; Helm, P A; Siewerdsen, J H.

Afiliação

Huang Y; Department of Biomedical Engineering, Johns Hopkins University, Baltimore MD, United States of America.
Uneri A; Department of Biomedical Engineering, Johns Hopkins University, Baltimore MD, United States of America.
Jones CK; The Malone Center for Engineering in Healthcare, Johns Hopkins University, Baltimore MD, United States of America.
Zhang X; Department of Biomedical Engineering, Johns Hopkins University, Baltimore MD, United States of America.
Ketcha MD; Department of Biomedical Engineering, Johns Hopkins University, Baltimore MD, United States of America.
Aygun N; Department of Radiology, Johns Hopkins University, Baltimore MD, United States of America.
Helm PA; Medtronic Inc., Littleton MA, United States of America.
Siewerdsen JH; Department of Biomedical Engineering, Johns Hopkins University, Baltimore MD, United States of America.

Phys Med Biol ; 66(12)2021 06 21.

Article em En | MEDLINE | ID: mdl-34082413

Assuntos

Coluna Vertebral; Tomografia Computadorizada por Raios X; Vértebras Lombares; Redes Neurais de Computação

Palavras-chave

deep learning; object detection; spine surgery; surgical data science; vertebrae labeling

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Coluna Vertebral / Tomografia Computadorizada por Raios X Tipo de estudo: Prognostic_studies Idioma: En Revista: Phys Med Biol Ano de publicação: 2021 Tipo de documento: Article País de afiliação: Estados Unidos

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