Abstract：
Diagnosis of Uveitic Macular Edema (UME) using Spectral Domain OCT (SD-OCT) is a promising method for early detection and monitoring of sight-threatening visual impairment. Viewing multiple B-scans and identifying biomarkers is challenging and time-consuming for clinical practitioners. To overcome these challenges, this paper proposes an image classification hybrid framework for predicting the presence of biomarkers such as intraretinal cysts (IRC), hyperreflective foci (HRF), hard exudates (HE) and neurosensory detachment (NSD) in OCT B-scans along with their severity.
A dataset of 10880 B-scans from 85 Uveitic patients is collected and graded by two board-certified ophthalmologists for the presence of biomarkers. A novel image classification framework, Dilated Depthwise Separable Convolution ResNet (DDSC-RN) with SVM classifier, is developed to achieve network compression with a larger receptive field that captures both low and high-level features of the biomarkers without loss of classification accuracy. The severity level of each biomarker is predicted from the feature map, extracted by the proposed DDSC-RN network.
The proposed hybrid model is evaluated using ground truth labels from the hospital. The deep learning model initially, identified the presence of biomarkers in B-scans. It achieved an overall accuracy of 98.64%, which is comparable to the performance of other state-of-the-art models, such as DRN-C-42 and ResNet-34. The SVM classifier then predicted the severity of each biomarker, achieving an overall accuracy of 89.3%.
A new hybrid model accurately identifies four retinal biomarkers on a tissue map and predicts their severity. The model outperforms other methods for identifying multiple biomarkers in complex OCT B-scans. This helps clinicians to screen multiple B-scans of UME more effectively, leading to better treatment outcomes.
A dataset of 10880 B-scans from 85 Uveitic patients is collected and graded by two board-certified ophthalmologists for the presence of biomarkers. A novel image classification framework, Dilated Depthwise Separable Convolution ResNet (DDSC-RN) with SVM classifier, is developed to achieve network compression with a larger receptive field that captures both low and high-level features of the biomarkers without loss of classification accuracy. The severity level of each biomarker is predicted from the feature map, extracted by the proposed DDSC-RN network.
The proposed hybrid model is evaluated using ground truth labels from the hospital. The deep learning model initially, identified the presence of biomarkers in B-scans. It achieved an overall accuracy of 98.64%, which is comparable to the performance of other state-of-the-art models, such as DRN-C-42 and ResNet-34. The SVM classifier then predicted the severity of each biomarker, achieving an overall accuracy of 89.3%.
A new hybrid model accurately identifies four retinal biomarkers on a tissue map and predicts their severity. The model outperforms other methods for identifying multiple biomarkers in complex OCT B-scans. This helps clinicians to screen multiple B-scans of UME more effectively, leading to better treatment outcomes.
摘要：
■使用谱域OCT（SD-OCT）诊断葡萄膜性黄斑水肿（UME）是早期检测和监测威胁视力的视力障碍的一种有前途的方法。查看多个B扫描和识别生物标志物对于临床从业者来说是具有挑战性和耗时的。为了克服这些挑战，本文提出了一种图像分类混合框架，用于预测视网膜内囊肿(IRC)等生物标志物的存在，超反射焦点（HRF），OCTB扫描中的硬渗出物（HE）和神经感觉脱离（NSD）及其严重程度。
■收集来自85名葡萄膜患者的10880个B扫描的数据集，并由两名董事会认证的眼科医生对生物标志物的存在进行分级。一种新颖的图像分类框架,带有SVM分类器的扩展深度可分离卷积ResNet（DDSC-RN），开发用于实现具有较大感受域的网络压缩，该感受域捕获生物标志物的低级和高级特征而不损失分类准确性。从特征图预测每个生物标志物的严重程度,通过提出的DDSC-RN网络提取。
■使用医院的地面实况标签对所提出的混合模型进行了评估。最初的深度学习模型，在B扫描中确定了生物标志物的存在。它达到了98.64％的整体精度，这与其他最先进的模型的性能相当，例如DRN-C-42和ResNet-34。然后SVM分类器预测每个生物标志物的严重程度，总体准确率为89.3%。
■一种新的混合模型在组织图上准确识别了四种视网膜生物标志物，并预测了它们的严重程度。该模型优于用于识别复杂OCTB扫描中的多种生物标志物的其他方法。这有助于临床医生更有效地筛选UME的多个B扫描，导致更好的治疗结果。
■收集来自85名葡萄膜患者的10880个B扫描的数据集，并由两名董事会认证的眼科医生对生物标志物的存在进行分级。一种新颖的图像分类框架,带有SVM分类器的扩展深度可分离卷积ResNet（DDSC-RN），开发用于实现具有较大感受域的网络压缩，该感受域捕获生物标志物的低级和高级特征而不损失分类准确性。从特征图预测每个生物标志物的严重程度,通过提出的DDSC-RN网络提取。
■使用医院的地面实况标签对所提出的混合模型进行了评估。最初的深度学习模型，在B扫描中确定了生物标志物的存在。它达到了98.64％的整体精度，这与其他最先进的模型的性能相当，例如DRN-C-42和ResNet-34。然后SVM分类器预测每个生物标志物的严重程度，总体准确率为89.3%。
■一种新的混合模型在组织图上准确识别了四种视网膜生物标志物，并预测了它们的严重程度。该模型优于用于识别复杂OCTB扫描中的多种生物标志物的其他方法。这有助于临床医生更有效地筛选UME的多个B扫描，导致更好的治疗结果。
