Automatic segmentation of multiple cardiovascular structures from cardiac computed tomography angiography images using deep learning

OBJECTIVES: To develop, demonstrate and evaluate an automated deep learning method for multiple cardiovascular structure segmentation. BACKGROUND: Segmentation of cardiovascular images is resource-intensive. We design an automated deep learning method for the segmentation of multiple structures from...

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Detalhes bibliográficos
Publicado no:PLoS One
Main Authors: Baskaran, Lohendran, Al’Aref, Subhi J., Maliakal, Gabriel, Lee, Benjamin C., Xu, Zhuoran, Choi, Jeong W., Lee, Sang-Eun, Sung, Ji Min, Lin, Fay Y., Dunham, Simon, Mosadegh, Bobak, Kim, Yong-Jin, Gottlieb, Ilan, Lee, Byoung Kwon, Chun, Eun Ju, Cademartiri, Filippo, Maffei, Erica, Marques, Hugo, Shin, Sanghoon, Choi, Jung Hyun, Chinnaiyan, Kavitha, Hadamitzky, Martin, Conte, Edoardo, Andreini, Daniele, Pontone, Gianluca, Budoff, Matthew J., Leipsic, Jonathon A., Raff, Gilbert L., Virmani, Renu, Samady, Habib, Stone, Peter H., Berman, Daniel S., Narula, Jagat, Bax, Jeroen J., Chang, Hyuk-Jae, Min, James K., Shaw, Leslee J.
Formato: Artigo
Idioma:Inglês
Publicado em: Public Library of Science 2020
Assuntos:
Research Article
Acesso em linha:https://ncbi.nlm.nih.gov/pmc/articles/PMC7202628/
https://ncbi.nlm.nih.gov/pubmed/32374784
https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1371/journal.pone.0232573
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