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首页> 外文期刊>BMC Medical Informatics and Decision Making >Two-stage framework for optic disc localization and glaucoma classification in retinal fundus images using deep learning
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Two-stage framework for optic disc localization and glaucoma classification in retinal fundus images using deep learning

机译：使用深度学习的视网膜眼底图像视盘定位和青光眼分类的两阶段框架

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摘要

With the advancement of powerful image processing and machine learning techniques, Computer Aided Diagnosis has become ever more prevalent in all fields of medicine including ophthalmology. These methods continue to provide reliable and standardized large scale screening of various image modalities to assist clinicians in identifying diseases. Since optic disc is the most important part of retinal fundus image for glaucoma detection, this paper proposes a two-stage framework that first detects and localizes optic disc and then classifies it into healthy or glaucomatous. The first stage is based on Regions with Convolutional Neural Network (RCNN) and is responsible for localizing and extracting optic disc from a retinal fundus image while the second stage uses Deep Convolutional Neural Network to classify the extracted disc into healthy or glaucomatous. Unfortunately, none of the publicly available retinal fundus image datasets provides any bounding box ground truth required for disc localization. Therefore, in addition to the proposed solution, we also developed a rule-based semi-automatic ground truth generation method that provides necessary annotations for training RCNN based model for automated disc localization. The proposed method is evaluated on seven publicly available datasets for disc localization and on ORIGA dataset, which is the largest publicly available dataset with healthy and glaucoma labels, for glaucoma classification. The results of automatic localization mark new state-of-the-art on six datasets with accuracy reaching 100% on four of them. For glaucoma classification we achieved Area Under the Receiver Operating Characteristic Curve equal to 0.874 which is 2.7% relative improvement over the state-of-the-art results previously obtained for classification on ORIGA dataset. Once trained on carefully annotated data, Deep Learning based methods for optic disc detection and localization are not only robust, accurate and fully automated but also eliminates the need for dataset-dependent heuristic algorithms. Our empirical evaluation of glaucoma classification on ORIGA reveals that reporting only Area Under the Curve, for datasets with class imbalance and without pre-defined train and test splits, does not portray true picture of the classifier’s performance and calls for additional performance metrics to substantiate the results.
机译：随着强大的图像处理和机器学习技术的发展，计算机辅助诊断在包括眼科在内的所有医学领域都变得越来越普遍。这些方法继续提供各种图像模式的可靠且标准化的大规模筛选，以帮助临床医生识别疾病。由于视盘是视网膜眼底图像对青光眼检测的最重要部分，因此本文提出了一个两阶段框架，该框架首先检测和定位视盘，然后将其分类为健康或青光眼。第一阶段基于具有卷积神经网络的区域（RCNN），负责从视网膜眼底图像定位和提取视盘，而第二阶段则使用深层卷积神经网络将提取的盘分类为健康或青光眼。不幸的是，没有一个公开的视网膜眼底图像数据集能够提供椎间盘定位所需要的任何边界框地面真相。因此，除了提出的解决方案之外，我们还开发了基于规则的半自动地面真相生成方法，该方法为训练基于RCNN的自动光盘定位模型提供了必要的注释。对七个公开可用的数据集进行了椎间盘定位，并在ORIGA数据集上进行了评估，ORIGA数据集是用于分类青光眼的最大的公开数据集，带有健康和青光眼标签。自动定位的结果在六个数据集上标记了最新技术，其中四个数据集的准确率达到了100％。对于青光眼分类，我们获得了接收器工作特征曲线下的面积等于0.874，这比以前在ORIGA数据集上获得的最新结果有2.7％的相对改善。一旦经过精心注释的数据训练，基于深度学习的用于光盘检测和定位的方法不仅可靠，准确且完全自动化，而且消除了依赖于数据集的启发式算法的需求。我们对ORIGA进行的青光眼分类的实证评估表明，对于类不平衡且没有预先定义的训练和测试拆分的数据集，仅报告曲线下面积并不代表分类器性能的真实情况，而是需要其他性能指标来证实分类器的性能。结果。

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