Background and Objectives

Pathological myopia (PM) is the seventh leading cause of blindness, with a reported global prevalence up to 3%. Early and automated PM detection from fundus images could aid to prevent blindness in a world population that is characterized by a rising myopia prevalence. We aim to assess the use of convolutional neural networks (CNNs) for the detection of PM and semantic segmentation of myopia-induced lesions from fundus images on a recently introduced reference data set.

Methods

This investigation reports on the results of CNNs developed for the recently introduced Pathological Myopia (PALM) dataset, which consists of 1200 images. Our CNN bundles lesion segmentation and PM classification, as the two tasks are heavily intertwined. Domain knowledge is also inserted through the introduction of a new Optic Nerve Head (ONH)-based prediction enhancement for the segmentation of atrophy and fovea localization. Finally, we are the first to approach fovea localization using segmentation instead of detection or regression models. Evaluation metrics include area under the receiver operating characteristic curve (AUC) for PM detection, Euclidean distance for fovea localization, and Dice and F1 metrics for the semantic segmentation tasks (optic disc, retinal atrophy and retinal detachment).

Results

Models trained with 400 available training images achieved an AUC of 0.9867 for PM detection, and a Euclidean distance of 58.27 pixels on the fovea localization task, evaluated on a test set of 400 images. Dice and F1 metrics for semantic segmentation of lesions scored 0.9303 and 0.9869 on optic disc, 0.8001 and 0.9135 on retinal atrophy, and 0.8073 and 0.7059 on retinal detachment, respectively.

Conclusions

We report a successful approach for a simultaneous classification of pathological myopia and segmentation of associated lesions. Our work was acknowledged with an award in the context of the “Pathological Myopia detection from retinal images” challenge held during the IEEE International Symposium on Biomedical Imaging (April 2019). Considering that (pathological) myopia cases are often identified as false positives and negatives in glaucoma deep learning models, we envisage that the current work could aid in future research to discriminate between glaucomatous and highly-myopic eyes, complemented by the localization and segmentation of landmarks such as fovea, optic disc and atrophy.

中文翻译：

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使用深度学习同时进行病变分割的病理性近视分类

背景和目标

病理性近视（PM）是失明的第七大原因，据报道全球患病率高达3％。从眼底图像中进行早期和自动的PM检测可以帮助预防以近视患病率上升为特征的世界人口失明。我们旨在评估卷积神经网络（CNN）在最近引入的参考数据集上从眼底图像中检测PM和近视眼诱发病变的语义分割的用途。

方法

这项调查报告了为最近引入的病理性近视（PALM）数据集开发的CNN结果，该数据集包含1200张图像。我们的CNN捆绑了病灶分割和PM分类，因为这两个任务紧密相关。通过引入一种新的基于视神经头（ONH）的预测增强功能来插入领域知识，可用于萎缩和中央凹定位的分割。最后，我们是第一个使用分割而不是检测或回归模型进行中央凹定位的方法。评估指标包括用于PM检测的接收器工作特征曲线（AUC）下的面积，用于中央凹定位的欧几里得距离以及用于语义分割任务（视盘，视网膜萎缩和视网膜脱离）的Dice和F1指标。

结果

用400张可用训练图像训练的模型在PM检测中达到了0.9867的AUC，在中央凹定位任务上的欧式距离为58.27像素（在400张图像的测试集上进行了评估）。视盘损伤的骰子和F1指标在视盘上分别为0.9303和0.9869，在视网膜萎缩上分别为0.8001和0.9135，在视网膜脱离上分别为0.8073和0.7059。

结论

我们报告了病理近视的同时分类和相关病变的分割的成功方法。在IEEE国际生物医学影像研讨会（2019年4月）期间举行的“从视网膜图像检出病理性近视”挑战赛中，我们的工作获得了奖项的认可。考虑到（病理性）近视眼病例在青光眼深度学习模型中通常被识别为假阳性和阴性，因此我们认为当前的工作可能会有助于未来的研究来区分青光眼和高度近视眼，并辅以标志物的定位和分割如中央凹，视盘和萎缩。