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- Title
Detection and Localization of Carina in X-ray Medical Images with Improved U-Net Model.
- Authors
WEN-LIN FAN; CHUNG-CHIAN HSU; CHIH-WEN LIN; JIA-SHIANG HE; TIN-KWANG LINs.; CHENG-CHUN WLF; ARTHUR CHANGR
- Abstract
After tracheal intubation for a patient in the intensive care unit. it is necessary to check for position appropriateness of the intubated endotracheal tube. Timely identification of dislocation and adjustment can prevent patients from morbidity and mortality. Manual checking ofthe chest X-ray images is time consuming and tedious. An automated way not only speeds the checking but also reduces doctor's work load. In this study, we propose a deep learning model U'+-Net, which yields good performance in semantic segmentation of tracheal and facilitates subsequent localization ofthe carina. In addition, an algorithm is proposed which locates the coordinate of carina from the segmented trachea. Experimental results show that the overall average error distance of detecting the position of carina is 0.29 cm, accuracy of the detection error within 0.5 cm and 1.0 cm are 85% and 99%, respectively, indicating that the proposed method is promising.
- Subjects
X-ray imaging; X-rays; DIAGNOSTIC imaging; INTENSIVE care patients; ENDOTRACHEAL tubes; TRACHEA intubation; DEEP learning
- Publication
Journal of Information Science & Engineering, 2024, Vol 40, Issue 3, p475
- ISSN
1016-2364
- Publication type
Article
- DOI
10.6688/JISE.20240540(3).0003