论文标题
从单眼视频中绘制眼表面，并应用了干眼症分级
Mapping the ocular surface from monocular videos with an application to dry eye disease grading
论文作者
论文摘要
干眼症（DED）的患病率为5％至50％，是眼科医生咨询的主要原因之一。 DED的诊断和量化通常依赖于通过缝隙灯 - 检查的眼表面分析。但是，评估是主观的且不可复制的。为了改善诊断，我们建议1）使用检查期间获得的视频记录在3D中跟踪眼表面，以及2）使用注册框架对严重程度进行评分。我们的注册方法使用无监督的图像到深度学习。这些方法从灯光和阴影中学习深度，并根据深度图估算姿势。但是，DED考试经历了未解决的挑战，包括移动的光源，透明的眼组织等。为了克服这些挑战并估算自我动机，我们实施了具有多种损失的联合CNN体系结构，这些损失包括了先前已知的信息，即通过语义分割和球形拟合以及眼睛的形状。所达到的跟踪误差优于最先进的，其平均欧几里得距离低至我们的测试集中图像宽度的0.48％。该注册将DED严重性分类提高了0.20 AUC差异。拟议的方法是第一个通过单眼视频监督来解决DED诊断的方法
With a prevalence of 5 to 50%, Dry Eye Disease (DED) is one of the leading reasons for ophthalmologist consultations. The diagnosis and quantification of DED usually rely on ocular surface analysis through slit-lamp examinations. However, evaluations are subjective and non-reproducible. To improve the diagnosis, we propose to 1) track the ocular surface in 3-D using video recordings acquired during examinations, and 2) grade the severity using registered frames. Our registration method uses unsupervised image-to-depth learning. These methods learn depth from lights and shadows and estimate pose based on depth maps. However, DED examinations undergo unresolved challenges including a moving light source, transparent ocular tissues, etc. To overcome these and estimate the ego-motion, we implement joint CNN architectures with multiple losses incorporating prior known information, namely the shape of the eye, through semantic segmentation as well as sphere fitting. The achieved tracking errors outperform the state-of-the-art, with a mean Euclidean distance as low as 0.48% of the image width on our test set. This registration improves the DED severity classification by a 0.20 AUC difference. The proposed approach is the first to address DED diagnosis with supervision from monocular videos
