Microaneurysms segmentation with a U-Net based on recurrent residual convolutional neural network

Microaneurysms (MAs) play an important role in the diagnosis of clinical diabetic retinopathy at the early stage. Annotation of MAs manually by experts is laborious and so it is essential to develop automatic segmentation methods. Automatic MA segmentation remains a challenging task mainly due to the low local contrast of the image and the small size of MAs. A deep learning-based method called U-Net has become one of the most popular methods for the medical image segmentation task. We propose an architecture for U-Net, named deep recurrent U-Net (DRU-Net), obtained by combining the deep residual model and recurrent convolutional operations into U-Net. In the MA segmentation task, DRU-Net can accumulate effective features much better than the typical U-Net. The proposed method is evaluated on two publicly available datasets: E-Ophtha and IDRiD. Our results show that the proposed DRU-Net achieves the best performance with 0.9999 accuracy value and 0.9943 area under curve (AUC) value on the E-Ophtha dataset. And on the IDRiD dataset, it has achieved 0.987 AUC value (to our knowledge, this is the first result of segmenting MAs on this dataset). Compared with other methods, such as U-Net, FCNN, and ResU-Net, our architecture (DRU-Net) achieves state-of-the-art performance.