Department of Ophthalmology, University Hospital Bonn, Ernst-Abbe-Str. 2, 53127, Bonn, Germany.
Department of Computer Science, University of Bonn, Endenicher Allee 19a, 53115, Bonn, Germany.
Sci Rep. 2020 Apr 30;10(1):7395. doi: 10.1038/s41598-020-63924-6.
Here, we investigate the extent to which re-implementing a previously published algorithm for OCT-based drusen quantification permits replicating the reported accuracy on an independent dataset. We refined that algorithm so that its accuracy is increased. Following a systematic literature search, an algorithm was selected based on its reported excellent results. Several steps were added to improve its accuracy. The replicated and refined algorithms were evaluated on an independent dataset with the same metrics as in the original publication. Accuracy of the refined algorithm (overlap ratio 36-52%) was significantly greater than the replicated one (overlap ratio 25-39%). In particular, separation of the retinal pigment epithelium and the ellipsoid zone could be improved by the refinement. However, accuracy was still lower than reported previously on different data (overlap ratio 67-76%). This is the first replication study of an algorithm for OCT image analysis. Its results indicate that current standards for algorithm validation do not provide a reliable estimate of algorithm performance on images that differ with respect to patient selection and image quality. In order to contribute to an improved reproducibility in this field, we publish both our replication and the refinement, as well as an exemplary dataset.
在这里,我们研究了重新实现以前发表的基于 OCT 的 黄斑病变定量算法在独立数据集上复制报告的准确性的程度。我们改进了该算法,以提高其准确性。通过系统的文献搜索,根据其报告的优异结果选择了一种算法。添加了几个步骤来提高其准确性。使用与原始出版物相同的指标,对独立数据集上的复制和改进算法进行了评估。改进算法(重叠比 36-52%)的准确性明显高于复制算法(重叠比 25-39%)。特别是,通过细化可以改善视网膜色素上皮和椭圆体带的分离。然而,准确性仍然低于之前在不同数据(重叠比 67-76%)上的报道。这是 OCT 图像分析算法的首次复制研究。其结果表明,当前的算法验证标准不能可靠地估计针对患者选择和图像质量不同的图像的算法性能。为了提高该领域的可重复性,我们发布了我们的复制和改进,以及一个示例数据集。