Automated Measurement of Optic Nerve Sheath Diameter Using Ocular Ultrasound Video
This incremental method addresses the need for accurate and operator-independent ONSD monitoring in clinical settings to support intracranial pressure evaluation.
The paper tackled the problem of automating optic nerve sheath diameter (ONSD) measurement from ocular ultrasound video to reduce reliance on operator skill, achieving a mean error of 0.04 and intraclass correlation coefficient of 0.782 compared to expert clinicians.
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.