分割
深度学习
气道
计算机科学
人工智能
内窥镜检查
睡眠(系统调用)
医学
放射科
外科
操作系统
作者
Chun‐Ting Chen,I‐Fang Chung,Ying‐Shuo Hsu,C. Wang,Yun‐Hsuan Lin,Sheng‐Yao Huang
标识
DOI:10.1109/icast57874.2023.10359292
摘要
Sleep endoscopy is a valuable diagnostic tool for assessing airway obstruction during sleep. Precise segmentation of airway structures is essential for clinical evaluation and treatment planning. In this study, we utilize a well-known deep learning method, U-Net++, for automatic velopharyngeal airway segmentation in sleep endoscopy images. Here U-Net++ is equipped with an attention mechanism to enhance the representational power of the model, the focus loss to handle the imbalanced situation of airway areas, and data augmentation techniques to introduce the data heterogeneity and then get better performance. Results demonstrate that using U-Net++ with data augmentation and focal loss significantly enhances segmentation performance, yielding a Dice Similarity Coefficient (DSC) of 0.912. Incorporating the attention mechanism leads to notable improvements in a challenging case, enhancing the DSC by 11.4%. Our findings highlight the potential of deep learning models in accurately segmenting velopharyngeal airway structures in sleep endoscopy images. Future research aims to further enhance the model's capabilities and address challenges posed by factors like saliva. In addition, we shall perform the segmentation task of oropharyngeal airway structures.
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