IEEE Trans Biomed Eng. 2019 Apr;66(4):1127-1136. doi: 10.1109/TBME.2018.2867636. Epub 2018 Aug 29.
At present, there are no objective techniques to quantify and describe laryngeal obstruction, and the reproducibility of subjective manual quantification methods is insufficient, resulting in diagnostic inaccuracy and a poor signal-to-noise ratio in medical research. In this work, a workflow is proposed to quantify laryngeal movements from laryngoscopic videos, to facilitate the diagnosis procedure.
The proposed method analyses laryngoscopic videos, and delineates glottic opening, vocal folds, and supraglottic structures, using a convolutional neural networks (CNNs) based algorithm. The segmentation is divided into two steps: A bounding box which indicates the region of interest (RoI) is found, followed by segmentation using fully convolutional networks (FCNs). The segmentation results are statistically quantified along the temporal dimension and processed using singular spectrum analysis (SSA), to extract clear objective information that can be used by the clinicians in diagnosis.
The segmentation was validated on 400 images from 20 videos acquired using different endoscopic systems from different patients. The results indicated significant improvements over using FCN only in terms of both processing speed (16 FPS vs. 8 FPS) and segmentation result statistics. Five clinical cases on patients have also been provided to showcase the quantitative analysis results using the proposed method.
The proposed method guarantees a robust and fast processing of laryngoscopic videos. Measurements of glottic angles and supraglottic index showed distinctive patterns in the provided clinical cases.
The proposed automated and objective method extracts important temporal laryngeal movement information, which can be used to aid laryngeal closure diagnosis.
目前尚无量化和描述喉阻塞的客观技术，主观手动量化方法的可重复性不足，导致诊断不准确，医学研究中的信噪比较差。在这项工作中，提出了一种从喉镜视频中量化喉运动的工作流程，以方便诊断程序。
所提出的方法分析喉镜视频，并使用基于卷积神经网络（CNN）的算法描绘声门开口、声带和喉上结构。分割分为两步：找到指示感兴趣区域（RoI）的边界框，然后使用全卷积网络（FCN）进行分割。沿时间维度对分割结果进行统计量化，并使用奇异谱分析（SSA）进行处理，以提取可用于临床医生诊断的清晰客观信息。
该分割在来自 20 个不同患者的不同内窥镜系统采集的 400 个图像上进行了验证。与仅使用 FCN 相比，在处理速度（16 FPS 与 8 FPS）和分割结果统计方面都有显著提高。还提供了五个临床病例，以展示使用所提出的方法进行的定量分析结果。
所提出的方法保证了喉镜视频的稳健和快速处理。在提供的临床病例中，声门角和喉上指数的测量显示出独特的模式。
所提出的自动和客观方法提取了重要的时间性喉运动信息，可用于辅助喉闭合诊断。
