Radiologist-Trained and -Tested (R2.2.4) Deep Learning Models for Identifying Anatomical Landmarks in Chest CT

(1) Background: Optimal anatomic coverage is important for radiation-dose optimization. We trained and tested (R2.2.4) two (R3-2) deep learning (DL) algorithms on a machine vision tool library platform (Cognex Vision Pro Deep Learning software) to recognize anatomic landmarks and classify chest CT a...

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Detalles Bibliográficos
Autores principales: Kaviani, Parisa, Bizzo, Bernardo C., Digumarthy, Subba R., Dasegowda, Giridhar, Karout, Lina, Hillis, James, Neumark, Nir, Kalra, Mannudeep K., Dreyer, Keith J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9407000/
https://www.ncbi.nlm.nih.gov/pubmed/36010194
http://dx.doi.org/10.3390/diagnostics12081844

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9407000/
https://www.ncbi.nlm.nih.gov/pubmed/36010194
http://dx.doi.org/10.3390/diagnostics12081844

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