SANet: A self-adaptive network for hyperreflective foci segmentation in retinal OCT images

Diabetic retinopathy (DR) is the most common chronic complication of diabetes and the first blinding eye disease in the working population. Hard exudates (HE) is an obvious symptom of diabetic retinopathy, which has high reflectivity to light and appears as hyperreflective foci (HRF) in optical coherence tomography (OCT) images. Based on the research and improvement of U-Net, this paper proposes a self-adaptive network (SANet) for HRF segmentation. There are two main improvements in the proposed SANet: (1) In order to simplify the learning process and enhance the gradient propagation, the ordinary convolution block in the encoder structure is replaced by a dual residual module (DRM). (2) The novel self-adaptive module (SAM) is embedded in the deep layer of the model, which enables the network to integrate local features and global dependencies adaptively, and makes it adapt to the irregular shape of HRF. The dataset consists of 112 2D OCT B-scan images, which were verified by four-fold cross validation. The mean and standard deviation of Dice similarity coefficient, Jaccard index, Sensitivity and Precision are 73.69 +/- 0.72%, 59.17 +/- 1.00%, 74.57 +/- 1.16% and 75.54 +/- 1.35%, respectively. The experimental results show that the proposed method can segment HRF successfully and the performance is better than the original U-Net.