# Multi-Path Recurrent U-Net Segmentation of Retinal Fundus Image
A novel patch-based output space adversarial learning framework (pOSAL) which could jointly and robustly segment OD and OC from different fundus image datasets was proposed in [21]. [PERSON] et al. [22] proposed a universal video disc and cup automatic segmentation method based on deep learning, which was a modification of the U-Net convolutional neural network. The fundus retinal image has a complex structure and low contrast; in particular, the lesion image contains a large amount of bleeding, micro tumors, and hard and soft exudates, which has a great impact on the segmentation of retinal blood vessels and the definition of the optic disc cup boundary. The methods proposed in the above literature usually have the following problems: (1) due to the large  
Figure 1: (**a**) is a healthy fundus image; (**b**)is an image of diabetic retinopathy with a large amount of bleeding and exudates on the retina; (**c**) is a normal fundus image, and the ratio of the vertical cup diameter (VCD) to vertical disk diameter (VDD) is relatively small; (**d**) is a glaucoma image with a narrow nerve-retinal margin band between the optic disc and optic cup. downsampling factor, the single-path downsampling process results in the loss of a large amount of feature information in the retinal image, which ultimately cannot be recovered; (2) insufficiently close cross-layer feature information contact leads to an insufficient understanding of the information--due to the single feature information of the same layer, the feature information cannot be complementarily fused, and ultimately a large amount of feature information is lost; (3) the feature extraction ability of the network structure is insufficient and it is difficult to restore low-level detailed feature information, which generates a great deal of noise in the segmented image; (4) the retinal blood vessel segmentation accuracy is low and the optic disc optic cup boundary segmentation is blurred. In view of the above problems, this paper proposes a multi-path recurrent U-Net architecture to achieve the segmentation of the optic disc optic cup. At the same time, the effectiveness of this method is verified on the retinal vessel segmentation data set DRIVE, and good results are achieved. The main contributions of this work are summarized as follows:  
1. A multi-path recurrent U-Net network architecture combining a convolutional neural network and a recurrent neural network is proposed. As shown in Figure 3a, this method implements the segmentation of retinal medical images. 2. Multi-branching the encoding path and the decoding path can more effectively obtain different semantic features. 3. The multi-path recurrent U-Net combines the recurrent neural network to sequence the multi-path output features in time to further improve the target features. 4. For the Drishti-GS1 dataset, the method for the segmentation of the OD and OC achieved state-of-the-art results. Our method has also been verified on the retinal blood vessel segmentation dataset DRIVE and achieved a good accuracy rate. The organizational structure of this article is as follows: Section 2 mainly discusses related work; in Section 3, the multi-path recurrent U-Net model proposed in this paper is described; Section 4 mainly describes the data set, experiments and results; and finally, Section 5 summarizes this article.