论文标题
使用血管分割图作为感应偏置的糖尿病性视网膜病变的自动分析
Automated analysis of diabetic retinopathy using vessel segmentation maps as inductive bias
论文作者
论文摘要
最近的研究表明，可以通过监测深血管复合物的血管变化来诊断糖尿病性视网膜病（DR）的早期阶段。在这项工作中，我们研究了一种基于光学连贯性层析成像（八八）图像的新型自动化DR分级方法。我们的工作结合了Octa扫描与其血管分割，然后用作特定于病变细分，图像质量评估和DR分级的特定任务网络的输入。为此，我们生成合成的八张图像来训练可以直接应用于真实八颗数据的分割网络。我们测试了MICCAI 2022的DR分析挑战（DRAC）的方法。在我们的实验中，所提出的方法的性能与基线模型同样出色。
Recent studies suggest that early stages of diabetic retinopathy (DR) can be diagnosed by monitoring vascular changes in the deep vascular complex. In this work, we investigate a novel method for automated DR grading based on optical coherence tomography angiography (OCTA) images. Our work combines OCTA scans with their vessel segmentations, which then serve as inputs to task specific networks for lesion segmentation, image quality assessment and DR grading. For this, we generate synthetic OCTA images to train a segmentation network that can be directly applied on real OCTA data. We test our approach on MICCAI 2022's DR analysis challenge (DRAC). In our experiments, the proposed method performs equally well as the baseline model.
