泪膜脂质层的动态跟踪
Tracking the Dynamics of the Tear Film Lipid Layer
摘要：
干眼病（DED）是最常见的眼部疾病之一：超过5%的美国成年人患有DED。泪膜不稳定性是DED的已知因素，并且被认为在很大程度上由覆盖并稳定泪膜的薄脂质层调节。为了帮助眼睛相关疾病的诊断，这项工作提出了一种新的范例，使用计算机视觉技术，以数字分析泪膜脂质层（TFLL）的蔓延。用显微干涉仪收集泪膜脂质层扩散的11个视频，并对子集进行注释。提出了一种基于多种支柱式计算机视觉技术的目标跟踪算法。我们的方法可以在https://easytear-dev.github.io/上找到。
英文摘要：
Dry Eye Disease (DED) is one of the most common ocular diseases: over five percent of US adults suffer from DED. Tear film instability is a known factor for DED, and is thought to be regulated in large part by the thin lipid layer that covers and stabilizes the tear film. In order to aid eye related disease diagnosis, this work proposes a novel paradigm in using computer vision techniques to numerically analyze the tear film lipid layer (TFLL) spread. Eleven videos of the tear film lipid layer spread are collected with a micro-interferometer and a subset are annotated. A tracking algorithm relying on various pillar computer vision techniques is developed. Our method can be found at https://easytear-dev.github.io/.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
