Fully Automated Segmentation of Head CT Neuroanatomy Using Deep Learning

PURPOSE: To develop a deep learning model that segments intracranial structures on head CT scans. MATERIALS AND METHODS: In this retrospective study, a primary dataset containing 62 normal noncontrast head CT scans from 62 patients (mean age, 73 years; age range, 27–95 years) acquired between August...

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Dettagli Bibliografici
Pubblicato in:Radiol Artif Intell
Autori principali: Cai, Jason C., Akkus, Zeynettin, Philbrick, Kenneth A., Boonrod, Arunnit, Hoodeshenas, Safa, Weston, Alexander D., Rouzrokh, Pouria, Conte, Gian Marco, Zeinoddini, Atefeh, Vogelsang, David C., Huang, Qiao, Erickson, Bradley J.
Natura: Artigo
Lingua:Inglês
Pubblicazione: Radiological Society of North America 2020
Soggetti:
Original Research
Accesso online:https://ncbi.nlm.nih.gov/pmc/articles/PMC8082409/
https://ncbi.nlm.nih.gov/pubmed/33937839
https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1148/ryai.2020190183
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