自动识别眼超视频最优帧，精准测量视神经鞘直径。
Automated Measurement of Optic Nerve Sheath Diameter Using Ocular Ultrasound Video
- 用KCF跟踪+SLIC分割找最佳视频帧
- 结合GMM与KL散度法实现高精度测量
- 误差极小，适合临床快速评估颅内压
颅内压升高是继发性脑损伤的重要生物标志物，视神经鞘直径（ONSD）与颅内压呈显著线性相关。频繁监测ONSD有助于动态评估颅内压。然而，传统测量依赖操作者经验，尤其在从超声视频序列中手动选择最佳帧并测量ONSD方面存在主观性。本文提出一种新方法：利用核相关滤波（KCF）跟踪算法和简单线性迭代聚类（SLIC）分割算法，自动识别视频序列中的最优帧；再通过高斯混合模型（GMM）结合基于KL散度的方法对视神经鞘进行建模与测量。与两位专家平均测量结果对比，该方法的平均误差为0.04，均方偏差为0.054，组内相关系数（ICC）达0.782。结果表明，该方法可实现高精度自动化ONSD测量，具有临床应用潜力。
原文摘要 · Abstract (English)
Objective. Elevated intracranial pressure (ICP) is recognized as a biomarker of secondary brain injury, with a significant linear correlation observed between optic nerve sheath diameter (ONSD) and ICP. Frequent monitoring of ONSD could effectively support dynamic evaluation of ICP. However, ONSD measurement is heavily reliant on the operator's experience and skill, particularly in manually selecting the optimal frame from ultrasound sequences and measuring ONSD. Approach. This paper presents a novel method to automatically identify the optimal frame from video sequences for ONSD measurement by employing the Kernel Correlation Filter (KCF) tracking algorithm and Simple Linear Iterative Clustering (SLIC) segmentation algorithm. The optic nerve sheath is mapped and measured using a Gaussian Mixture Model (GMM) combined with a KL-divergence-based method. Results. When compared with the average measurements of two expert clinicians, the proposed method achieved a mean error, mean squared deviation, and intraclass correlation coefficient (ICC) of 0.04, 0.054, and 0.782, respectively. Significance. The findings suggest that this method provides highly accurate automated ONSD measurements, showing potential for clinical application.
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