Retinal Vessel Segmentation With Vae Reconstruction And Multi-Scale Context Extractor

The clinical diagnosis of eye disorders including diabetic retinopathy relies heavily on retinal vessel segmentation. CNN-based methods are the preferred approaches for retina vessel segmentation in recent years, but they are data hungry and prone to overfitting on the training set and achieving sub-optimal results on the validation set or the test set. Taking this into consideration, we propose to integrate a variational autoencoder reconstruction branch to pose extra regularization on the shared encoder and increase the generalization ability of networks. Furthermore, to deal with the unbalanced vessel scale distribution, a multi-scale context extractor is carefully designed, which employed the regular convolution and dilated convolution to extract multi-scale context and utilized different fusion method to obtain better complementary features. Extensive experiment results demonstrate that our proposed method achieves comparable state-of-the-art performance on the popular datasets: DRIVE and CHASEDB1.