论文标题
UW-OCTA糖尿病性视网膜病变级评估的半监督语义分割方法
Semi-Supervised Semantic Segmentation Methods for UW-OCTA Diabetic Retinopathy Grade Assessment
论文作者
论文摘要
糖尿病患者比健康的人更有可能患上糖尿病性视网膜病变（DR）。但是，DR是失明的主要原因。目前，诊断糖尿病性视网膜病主要依赖经验丰富的临床医生来认识彩色眼底图像中的精美特征。这是一项耗时的任务。因此，在本文中，为了促进UW-OCTA DR自动检测的发展，我们提出了一种新型的半监督语义分割方法，用于UW-OCTA DR图像级评估。首先，该方法使用MAE算法对UW-OCTA DR级评估数据集进行半监督预培训，以在UW-OCTA图像中挖掘监督信息，从而减轻对标记数据的需求。其次，为了更全面地挖掘UW-OCTA图像中每个区域的病变特征，本文通过部署具有不同视觉特征处理策略的三种算法来构建交叉叠加集合DR Tissue Sementation算法。该算法包含三个子词素，即预先培训的MAE，Convnext和Segformer。基于这三个子算法的缩写，该算法可以命名为MCS-DRNET。最后，我们使用MCS-DRNET算法作为检查员，检查并修改DR等级评估算法的初步评估结果。实验结果表明，MCS-DRNET V1和V2的平均骰子相似系数分别为0.5161和0.5544。 DR分级评估的二次加权Kappa为0.7559。我们的代码将很快发布。
People with diabetes are more likely to develop diabetic retinopathy (DR) than healthy people. However, DR is the leading cause of blindness. At present, the diagnosis of diabetic retinopathy mainly relies on the experienced clinician to recognize the fine features in color fundus images. This is a time-consuming task. Therefore, in this paper, to promote the development of UW-OCTA DR automatic detection, we propose a novel semi-supervised semantic segmentation method for UW-OCTA DR image grade assessment. This method, first, uses the MAE algorithm to perform semi-supervised pre-training on the UW-OCTA DR grade assessment dataset to mine the supervised information in the UW-OCTA images, thereby alleviating the need for labeled data. Secondly, to more fully mine the lesion features of each region in the UW-OCTA image, this paper constructs a cross-algorithm ensemble DR tissue segmentation algorithm by deploying three algorithms with different visual feature processing strategies. The algorithm contains three sub-algorithms, namely pre-trained MAE, ConvNeXt, and SegFormer. Based on the initials of these three sub-algorithms, the algorithm can be named MCS-DRNet. Finally, we use the MCS-DRNet algorithm as an inspector to check and revise the results of the preliminary evaluation of the DR grade evaluation algorithm. The experimental results show that the mean dice similarity coefficient of MCS-DRNet v1 and v2 are 0.5161 and 0.5544, respectively. The quadratic weighted kappa of the DR grading evaluation is 0.7559. Our code will be released soon.
