Deep Learning Models for Automatic Upper Airway Segmentation and Minimum Cross-Sectional Area Localisation in Two-Dimensional Images

Objective: To develop and validate convolutional neural network algorithms for automatic upper airway segmentation and minimum cross-sectional area (CSAmin) localisation in two-dimensional (2D) radiographic airway images. Materials and Methods: Two hundred and one 2D airway images acquired using con...

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Detalles Bibliográficos
Autores principales: Chu, Guang, Zhang, Rongzhao, He, Yingqing, Ng, Chun Hown, Gu, Min, Leung, Yiu Yan, He, Hong, Yang, Yanqi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10451171/
https://www.ncbi.nlm.nih.gov/pubmed/37627800
http://dx.doi.org/10.3390/bioengineering10080915

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10451171/
https://www.ncbi.nlm.nih.gov/pubmed/37627800
http://dx.doi.org/10.3390/bioengineering10080915

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