Adal Kedir M, van Etten Peter G, Martinez Jose P, van Vliet Lucas J, Vermeer Koenraad A
Rotterdam Ophthalmic Institute, Rotterdam, The Netherlands Quantitative Imaging Group, Delft University of Technology, Delft, The Netherlands.
Rotterdam Eye Hospital, Rotterdam, The Netherlands.
Invest Ophthalmol Vis Sci. 2015 Feb 3;56(3):1805-12. doi: 10.1167/iovs.14-15949.
We evaluated the accuracy of a recently developed fundus image registration method (Weighted Vasculature Registration, or WeVaR) and compared it to two top-ranked state-of-the-art commercial fundus mosaicking programs (i2k Retina, DualAlign LLC, and Merge Eye Care PACS, formerly named OIS AutoMontage) in the context of diabetic retinopathy (DR) screening.
Fundus images of 70 diabetic patients who visited the Rotterdam Eye Hospital in 2012 and 2013 for a DR screening program were registered by all three programs. The registration results were used to produce mosaics from fundus photos that were normalized for luminance and contrast to improve the visibility of small details. These mosaics subsequently were evaluated and ranked by two expert graders to assess the registration accuracy.
Merge Eye Care PACS had high registration failure rates compared to WeVaR and i2k Retina (P = 8 × 10(-6) and P = 0.002, respectively). WeVaR showed significantly higher registration accuracy than i2k Retina in intravisit (P ≤ 0.0036) and intervisit (P ≤ 0.0002) mosaics. Therefore, fundus mosaics processed by WeVaR were more likely to have a higher score (odds ratio [OR] = 2.5, P = 10(-5) for intravisit and OR = 2.2, P = 0.006 for intervisit mosaics). WeVaR was preferred more often by the graders than i2k Retina (OR = 6.1, P = 7 × 10(-6)).
WeVaR produced intra- and intervisit fundus mosaics with higher registration accuracy than Merge Eye Care PACS and i2k Retina. Merge Eye Care PACS had higher registration failures than the other two programs. Highly accurate registration methods, such as WeVaR, may potentially be used for more efficient human grading and in computer-aided screening systems for detecting DR progression.
我们评估了一种最近开发的眼底图像配准方法（加权血管配准，或WeVaR）的准确性，并在糖尿病视网膜病变（DR）筛查的背景下，将其与两个排名靠前的最先进的商业眼底拼接程序（i2k Retina，DualAlign LLC，以及之前名为OIS AutoMontage的Merge Eye Care PACS）进行比较。
2012年和2013年到鹿特丹眼科医院参加DR筛查项目的70名糖尿病患者的眼底图像由这三个程序进行配准。配准结果用于从眼底照片生成拼接图，这些照片进行了亮度和对比度归一化处理，以提高小细节的可见性。随后，由两名专家分级员对这些拼接图进行评估和排名，以评估配准准确性。
与WeVaR和i2k Retina相比，Merge Eye Care PACS的配准失败率较高（分别为P = 8×10⁻⁶和P = 0.002）。在就诊内（P≤0.0036）和就诊间（P≤0.0002）的拼接图中，WeVaR显示出比i2k Retina显著更高的配准准确性。因此，由WeVaR处理的眼底拼接图更有可能获得更高的分数（就诊内的优势比[OR] = 2.5，P = 10⁻⁵；就诊间拼接图的OR = 2.2，P = 0.006）。分级员比i2k Retina更常选择WeVaR（OR = 6.1，P = 7×10⁻⁶）。
WeVaR生成的就诊内和就诊间眼底拼接图的配准准确性高于Merge Eye Care PACS和i2k Retina。Merge Eye Care PACS的配准失败率高于其他两个程序。像WeVaR这样的高精度配准方法可能潜在地用于更高效的人工分级以及检测DR进展的计算机辅助筛查系统中。
