Department of Ophthalmology, University Hospital Zurich, University of Zurich, Zurich, Switzerland.
Department of Artificial Intelligence and Systems Engineering, Riga Technical University, Riga, Latvia.
Acta Neurochir Suppl. 2022;134:303-311. doi: 10.1007/978-3-030-85292-4_34.
Spontaneous venous pulsations (SVP) are a common finding in healthy people. The absence of SVP is associated with rapid progression in glaucoma and increased intracranial pressure. Traditionally, SVP has been documented qualitatively by clinicians during biomicroscopy. Nowadays numerous imaging devices recording the fundus exist. Hence, video data for objectification of SVP is readily available. Still, these clinical datasets are afflicted with various quality issues and artifacts. In this machine vision based study, we explore methods to overcome challenges in identifying SVP in fundus videos of varying quality and provide a detailed protocol thereof. Hereby, we aim to lower the burden of access of implementing machine vision in clinical video datasets and quantification of SVP.
自发性静脉搏动 (SVP) 在健康人群中很常见。SVP 的缺失与青光眼的快速进展和颅内压升高有关。传统上,临床医生在生物显微镜检查期间定性记录 SVP。如今,存在许多记录眼底的成像设备。因此,SVP 的客观化的视频数据很容易获得。尽管如此,这些临床数据集受到各种质量问题和伪影的困扰。在这项基于机器视觉的研究中,我们探索了在不同质量的眼底视频中识别 SVP 的方法,并提供了详细的方案。在此,我们旨在降低在临床视频数据集中实施机器视觉和 SVP 量化的难度。