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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Publicat a:PLoS One
Autors principals: 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.
Format: Artigo
Idioma:Inglês
Publicat: Public Library of Science 2020
Matèries:
Research Article
Accés en línia: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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