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

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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Bibliographic Details
Main Authors: Guang Chu, Rongzhao Zhang, Yingqing He, Chun Hown Ng, Min Gu, Yiu Yan Leung, Hong He, Yanqi Yang
Format: Article
Language:English
Published: MDPI AG 2023-08-01
Series:Bioengineering
Subjects:
artificial intelligence
cone-beam computed tomography
convolutional neural networks
airway segmentation
CSAmin localisation
Online Access:https://www.mdpi.com/2306-5354/10/8/915

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https://www.mdpi.com/2306-5354/10/8/915

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