论文标题
使用深度学习的病理近视分类和同时病变分割
Pathological myopia classification with simultaneous lesion segmentation using deep learning
论文作者
论文摘要
这项调查报告了针对最近引入的病理性近视（Palm）数据集开发的卷积神经网络的结果，该数据集由1200张眼底图像组成。我们提出了一种新的视神经头（ONH）基于基于萎缩和中央凹的分割的预测增强。经过400次可用训练图像训练的模型在Fovea本地化任务上获得了0.9867的AUC，用于病理近视分类为0.9867，欧几里得距离为58.27像素，并在400张图像的测试集上进行了评估。病变语义分割的骰子和F1指标分别在视盘上得分0.9303和0.9869，视网膜萎缩的0.8001和0.9135分别为0.9135，视网膜脱离为0.8073和0.7059。在IEEE国际生物医学成像研讨会上举行的“病理近视检测中的病理近视检测”挑战（2019年4月）中，我们的工作获得了奖项。考虑到（病理）肌病病例通常被确定为青光眼分类系统中的假阳性和负面因素，我们设想当前的工作可以帮助将来的研究区分glaucomation和高度杂型眼睛，并通过饲养和绒毛，视盘，光学盘和动物等地标的本地化和分割来互补。
This investigation reports on the results of convolutional neural networks developed for the recently introduced PathologicAL Myopia (PALM) dataset, which consists of 1200 fundus images. We propose a new Optic Nerve Head (ONH)-based prediction enhancement for the segmentation of atrophy and fovea. Models trained with 400 available training images achieved an AUC of 0.9867 for pathological myopia classification, 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. 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 classification systems for glaucoma, we envision 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.
