Department of Computer and Software Engineering, Polytechnique Montréal, Montréal, QC, Canada.
Department of Ophthalmology, Université de Montréal, Montréal, Canada.
Sci Data. 2024 Aug 23;11(1):914. doi: 10.1038/s41597-024-03739-6.
Reliable automatic diagnosis of Diabetic Retinopathy (DR) and Macular Edema (ME) is an invaluable asset in improving the rate of monitored patients among at-risk populations and in enabling earlier treatments before the pathology progresses and threatens vision. However, the explainability of screening models is still an open question, and specifically designed datasets are required to support the research. We present MAPLES-DR (MESSIDOR Anatomical and Pathological Labels for Explainable Screening of Diabetic Retinopathy), which contains, for 198 images of the MESSIDOR public fundus dataset, new diagnoses for DR and ME as well as new pixel-wise segmentation maps for 10 anatomical and pathological biomarkers related to DR. This paper documents the design choices and the annotation procedure that produced MAPLES-DR, discusses the interobserver variability and the overall quality of the annotations, and provides guidelines on using the dataset in a machine learning context.
可靠的糖尿病性视网膜病变(DR)和黄斑水肿(ME)自动诊断是提高高危人群中受监测患者比例的宝贵手段,并且可以在病变进展并威胁视力之前更早地进行治疗。然而,筛选模型的可解释性仍然是一个悬而未决的问题,并且需要专门设计的数据集来支持研究。我们提出了 MAPLES-DR(MESSIDOR 解剖学和病理学标签用于可解释性糖尿病性视网膜病变筛查),它包含 MESSIDOR 公共眼底数据集的 198 张图像,对 DR 和 ME 进行了新的诊断,并对 10 个与 DR 相关的解剖学和病理学生物标志物进行了新的像素级分割图。本文记录了产生 MAPLES-DR 的设计选择和注释过程,讨论了观察者间的变异性和注释的整体质量,并提供了在机器学习环境中使用数据集的指南。