Appl Opt. 2021 Aug 10;60(23):6761-6768. doi: 10.1364/AO.426053.
Optical coherence tomography (OCT) technology can obtain a clear retinal structure map, which is greatly beneficial for the diagnosis of retinopathy. Ophthalmologists can use OCT technology to analyze information about the retina's internal structure and changes in retinal thickness. Therefore, segmentation of retinal layers in images and screening for retinal diseases have become important goals in OCT scanning. In this paper, we propose the multiscale dual attention (MSDA)-UNet network, an MSDA mechanism network for OCT lesion area segmentation. The MSDA-UNet network introduces position and multiscale channel attention modules to calculate a global reference for each pixel prediction. The network can extract the lesion area information of OCT images of different scales and perform end-to-end segmentation of the OCT retinopathy area. The network framework was trained and tested on the same OCT dataset and compared with other OCT fluid segmentation methods to assess its effectiveness.
光学相干断层扫描（OCT）技术可以获得清晰的视网膜结构图谱，这对视网膜病变的诊断非常有益。眼科医生可以利用 OCT 技术来分析视网膜内部结构和视网膜厚度变化的信息。因此，图像中的视网膜层分割和视网膜疾病筛查已成为 OCT 扫描的重要目标。在本文中，我们提出了多尺度双注意力（MSDA）-UNet 网络，这是一种用于 OCT 病变区域分割的 MSDA 机制网络。MSDA-UNet 网络引入了位置和多尺度通道注意力模块，为每个像素预测计算全局参考。该网络可以提取不同尺度的 OCT 图像的病变区域信息，并对 OCT 视网膜病变区域进行端到端分割。该网络框架在相同的 OCT 数据集上进行训练和测试，并与其他 OCT 液体积分方法进行比较，以评估其有效性。
