Shahsavari Navid, Zare Bidaki Ehsan, Wong Alexander, Murphy Paul J
School of Optometry and Vision Science, University of Waterloo, Waterloo, ON N2L 3G1, Canada.
System Design Engineering Department, University of Waterloo, Waterloo, ON N2L 3G1, Canada.
J Imaging. 2025 Apr 23;11(5):131. doi: 10.3390/jimaging11050131.
The assessment of ocular surface temperature (OST) plays a pivotal role in the diagnosis and management of various ocular diseases. This paper introduces significant enhancements to the ThermOcular system, initially developed for precise OST measurement using infrared (IR) thermography. These advancements focus on accuracy improvements that reduce user dependency and increase the system's diagnostic capabilities. A novel addition to the system includes the use of EyeTags, which assist clinicians in selecting control points more easily, thus reducing errors associated with manual selection. Furthermore, the integration of state-of-the-art semantic segmentation models trained on the newest dataset is explored. Among these, the OCRNet-HRNet-w18 model achieved a segmentation accuracy of 96.21% MIOU, highlighting the effectiveness of the improved pipeline. Additionally, the challenge of eliminating eyelashes in IR frames, which cause artifactual measurement errors in OST assessments, is addressed. Through a newly developed method, the influence of eyelashes is eliminated, thereby enhancing the precision of temperature readings. Moreover, an algorithm for blink detection and elimination is implemented, significantly improving upon the basic methods previously utilized. These innovations not only enhance the reliability of OST measurements, but also contribute to the system's efficiency and diagnostic accuracy, marking a significant step forward in ocular health monitoring and diagnostics.
眼表温度（OST）评估在各种眼科疾病的诊断和管理中起着关键作用。本文介绍了对ThermOcular系统的重大改进，该系统最初是为使用红外（IR）热成像进行精确的OST测量而开发的。这些改进集中在提高准确性上，减少了用户依赖性并提高了系统的诊断能力。该系统的一个新功能是使用EyeTags，它有助于临床医生更轻松地选择控制点，从而减少与手动选择相关的误差。此外，还探索了在最新数据集上训练的最先进语义分割模型的集成。其中，OCRNet-HRNet-w18模型的分割准确率达到了96.21%的平均交并比（MIOU），突出了改进流程的有效性。此外，还解决了消除红外图像帧中睫毛的挑战，睫毛会在OST评估中导致人为测量误差。通过一种新开发的方法，消除了睫毛的影响，从而提高了温度读数的精度。此外，还实现了一种用于眨眼检测和消除的算法，对以前使用的基本方法有了显著改进。这些创新不仅提高了OST测量的可靠性，还提高了系统的效率和诊断准确性，标志着眼部健康监测和诊断向前迈出了重要一步。
