RAG-FW: A Hybrid Convolutional Framework for the Automated Extraction of Retinal Lesions and Lesion-Influenced Grading of Human Retinal Pathology
RAG-FW: A Hybrid Convolutional Framework for the Automated Extraction of Retinal Lesions and Lesion-Influenced Grading of Human Retinal Pathology
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DOI：
10.1109/jbhi.2020.2982914
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发表时间：
2021-01-01
影响因子：
7.7
通讯作者：
Nazir, Muhammad Noman
中科院分区：
文献类型：
--
作者：
Hassan, Taimur;Akram, Muhammad Usman;Nazir, Muhammad Noman
视网膜病变的识别在视网膜病变的准确分类和分级中起着至关重要的作用。许多研究人员提出了基于光学相干断层扫描（OCT）的视网膜图像分析在过去的研究。然而，据我们所知，目前还没有框架可以从多供应商OCT扫描中提取视网膜病变并将其用于人类视网膜的直观严重程度分级。为了弥补这一不足，我们提出了一个深视网膜分析和分级框架（RAG-FW）。RAG-FW是一种混合卷积框架，可从OCT扫描中提取多个视网膜病变，并根据临床标准将其用于病变影响的视网膜病变分级。RAG-FW已经在来自五个高度复杂的公开数据集的43，613次扫描中进行了严格的测试，其中包含多供应商扫描，在提取视网膜病变时，其平均交叉-联合得分为0.8055，视网膜病变的准确性为98.70。
The identification of retinal lesions plays a vital role in accurately classifying and grading retinopathy. Many researchers have presented studies on optical coherence tomography (OCT) based retinal image analysis over the past. However, to the best of our knowledge, there is no framework yet available that can extract retinal lesions from multi-vendor OCT scans and utilize them for the intuitive severity grading of the human retina. To cater this lack, we propose a deep retinal analysis and grading framework (RAG-FW). RAG-FW is a hybrid convolutional framework that extracts multiple retinal lesions from OCT scans and utilizes them for lesion-influenced grading of retinopathy as per the clinical standards. RAG-FW has been rigorously tested on 43,613 scans from five highly complex publicly available datasets, containing multi-vendor scans, where it achieved the mean intersection-over-union score of 0.8055 for extracting the retinal lesions and the accuracy of 98.70 for the correct severity grading of retinopathy.