RSAP-Net: joint optic disc and cup segmentation with a residual spatial attention path module and MSRCR-PT pre-processing algorithm

Abstract

Background Glaucoma can cause irreversible blindness to people’s eyesight. Since there are no symptoms in its early stage, it is particularly important to accurately segment the optic disc (OD) and optic cup (OC) from fundus medical images for the screening and prevention of glaucoma. In recent years, the mainstream method of OD and OC segmentation is convolution neural network (CNN). However, most existing CNN methods segment OD and OC separately and ignore the a priori information that OC is always contained inside the OD region, which makes the segmentation accuracy of most methods not high enough. Methods This paper proposes a new encoder–decoder segmentation structure, called RSAP-Net, for joint segmentation of OD and OC. We first designed an efficient U-shaped segmentation network as the backbone. Considering the spatial overlap relationship between OD and OC, a new Residual spatial attention path is proposed to connect the encoder–decoder to retain more characteristic information. In order to further improve the segmentation performance, a pre-processing method called MSRCR-PT (Multi-Scale Retinex Colour Recovery and Polar Transformation) has been devised. It incorporates a multi-scale Retinex colour recovery algorithm and a polar coordinate transformation, which can help RSAP-Net to produce more refined boundaries of the optic disc and the optic cup. Results The experimental results show that our method achieves excellent segmentation performance on the Drishti-GS1 standard dataset. In the OD and OC segmentation effects, the F1 scores are 0.9752 and 0.9012, respectively. The BLE are 6.33 pixels and 11.97 pixels, respectively. Conclusions This paper presents a new framework for the joint segmentation of optic discs and optic cups, called RSAP-Net. The framework mainly consists of a U-shaped segmentation skeleton and a residual space attention path module. The design of a pre-processing method called MSRCR-PT for the OD/OC segmentation task can improve segmentation performance. The method was evaluated on the publicly available Drishti-GS1 standard dataset and proved to be effective.

Self-supervised pre-training for joint optic disc and cup segmentation via attention-aware network

“…Following the previous works [ 8 , 27 ] strictly, we evaluate the performance of the proposed method using the F1 score, Boundary distance Localization Error (BLE) and the Dice coefficients (DC). Among them, the definition of F1 can be computed as follows: where Precision = and Recall = .…”

“…To this end, the automatic retinal fundus photography strategy [ 8 , 9 ] using deep neural networks becomes popular, which can help doctors to screen glaucoma. As shown in Fig.…”

Self-supervised Pre-training for Joint Optic Disc and Cup Segmentation via Attention-Aware Network

“…4 School of Electrical Automation and Information Engineering, Tianjin University, 300000 Tianjin, China. 5 chool of Mathematical Sciences, Xiamen University, 361000 Xiamen, China.…”

“…To this end, the automatic retinal fundus photography strategy [5,6] using deep neural networks becomes popular, which can help doctors to screen glaucoma. As shown in Figure 1, the retinal fundus image shoots the main structure of the fundus including the optic disc (OD) and optic cup (OC).…”

Research on the Small Target Recognition Method of Automobile Tire Marking Points Based on Improved YOLOv5s

“…In Equation ( 3), R MSR i (x, y) represents the recovered image of the ith channel after using multi-scale filtering, G n (x, y) is a single-scale Gaussian filter, λ represents the weight, and m is the number of scales [11][12][13]. A comparison of the images before and after processing is shown in Figure 4.…”