A segmentation network based on CNNs for identifying laryngeal structures in video laryngoscope images.

Journal: Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Published Date: May 29, 2025

Abstract

Video laryngoscopes have become increasingly vital in tracheal intubation, providing clear imaging that significantly improves success rates, especially for less experienced clinicians. However, accurate recognition of laryngeal structures remains challenging, which is critical for successful first-attempt intubation in emergency situations. This paper presents MPE-UNet, a deep learning model designed for precise segmentation of laryngeal structures from video laryngoscope images, aiming to assist clinicians in performing tracheal intubation more accurately and efficiently. MPE-UNet follows the classic U-Net architecture, which features an encoder-decoder structure and enhances it with advanced modules and innovative techniques at every stage. In the encoder, we designed an improved multi-scale feature extraction module, which better processes complex throat images. Additionally, a pyramid fusion attention module was incorporated into the skip connections, enhancing the model's ability to capture details by dynamically weighting and merging features from different levels. Moreover, a plug-and-play attention mechanism module was integrated into the decoder, further refining the segmentation process by focusing on important features. The experimental results show that the performance of the proposed method outperforms state-of-the-art methods.

Authors

  • Jinjing Wu
    School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China.
  • Wenhui Guo
    Department of Neuro-Oncology, Cancer Center, Beijing Tiantan Hospital, Capital Medical University, No.119 West Nansihuan Road, Beijing, 100070, China.
  • Zhanheng Chen
    College of Mathematics and Statistics, Yili Normal University, Yining, 835000 Xinjiang, China.
  • Huixiu Hu
    Department of Nursing, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, PR China.
  • Houfeng Li
    School of Graduate, Hebei North University, Hebei, China.
  • Ying Zhang
    Department of Nephrology, Nanchong Central Hospital Affiliated to North Sichuan Medical College, Nanchong, China.
  • Jing Huang
    Department of Nephrology, The Second Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China.
  • Long Liu
    Department of Ultrasound, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
  • Zhenghao Xu
    Medical Affairs Office, Second Affiliated Hospital of Naval Medical University, Shanghai, China.
  • Tianying Xu
    School of Anesthesiology, Naval Medical University, Shanghai, China.
  • Miao Zhou
    Department of Anesthesiology, The Affiliated Cancer Hospital of Nanjing Medical University, Jiangsu Cancer Hospital, Jiangsu, China.
  • Chenglong Zhu
    School of Anesthesiology, Naval Medical University, Shanghai, China.
  • Haipo Cui
    School of Medical Instruments and Food Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China.
  • Wenyun Xu
    Department of Anesthesiology, Second Affiliated Hospital of Naval Medical University, Shanghai, China. Electronic address: xuwenyun@smmu.edu.cn.
  • Zui Zou
    Faculty of Anesthesiology, Changhai Hospital of Naval Medical University, Shanghai, China; School of Anesthesiology, Naval Medical University, Shanghai, China. Electronic address: zouzui@smmu.edu.cn.

Keywords

No keywords available for this article.

External Resources

Popular Topics
Recent Journals