Mæstad Rune, Hanan Abdul, Kristian Kvidaland Haakon, Clemm Hege, Arghandeh Reza
Faculty of Engineering and Science, Western Norway University of Applied Sciences, Bergen, Vestland, Norway.
Faculty of Health and Social Sciences, Western Norway University of Applied Sciences, Bergen, Vestland, Norway.
Front Digit Health. 2025 Jul 11;7:1459136. doi: 10.3389/fdgth.2025.1459136. eCollection 2025.
Manual diagnostic methods for assessing exercise-induced laryngeal obstruction (EILO) contain human bias and can lead to subjective decisions. Several studies have proposed machine learning methods for segmenting laryngeal structures to automate and make diagnostic outcomes more objective. Four state-of-the-art models for laryngeal image segmentation are implemented, trained, and compared using our pre-processed dataset containing laryngeal images derived from continuous laryngoscopy exercise-test (CLE-test) data. These models include both convolutional-based and transformer-based methods. We propose a new framework called LarynxFormer, consisting of a pre-processing pipeline, transformer-based segmentation, and post-processing of laryngeal images. This study contributes to the investigation of using machine learning as a diagnostic tool for EILO. Furthermore, we show that a transformer-based approach for larynx segmentation outperforms conventional state-of-the-art image segmentation methods in terms of performance metrics and computational speed, demonstrating up to 2x faster inference time compared to the other methods.
用于评估运动性喉梗阻（EILO）的手动诊断方法存在人为偏差，可能导致主观判断。多项研究提出了用于分割喉部结构的机器学习方法，以实现自动化并使诊断结果更客观。我们使用包含来自连续喉镜运动测试（CLE测试）数据的喉部图像的预处理数据集，实现、训练并比较了四种用于喉部图像分割的先进模型。这些模型包括基于卷积的方法和基于Transformer的方法。我们提出了一个名为LarynxFormer的新框架，它由一个预处理管道、基于Transformer的分割以及喉部图像的后处理组成。本研究有助于将机器学习用作EILO诊断工具的研究。此外，我们表明，基于Transformer的喉部分割方法在性能指标和计算速度方面优于传统的先进图像分割方法，与其他方法相比，推理时间快达2倍。
