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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Detalles Bibliográficos
Autores principales: 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: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2020
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7202628/
https://www.ncbi.nlm.nih.gov/pubmed/32374784
http://dx.doi.org/10.1371/journal.pone.0232573

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7202628/
https://www.ncbi.nlm.nih.gov/pubmed/32374784
http://dx.doi.org/10.1371/journal.pone.0232573

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