MRSNet: Joint consistent optic disc and cup segmentation based on large kernel residual convolutional attention and self-attention

Abstract

Glaucoma is a prevalent eye disease that is responsible for causing blindness worldwide. To diagnose glaucoma, the cup-to-disc ratio (CDR) is an important factor. We introduce MRSNet, a novel segmentation network that incorporates encoding and decoding structures. The key innovation of this network is the application of convolutional block with large kernel convolutional attention to the task of medical image segmentation for optic cup and disc. By combining the benefits of residual and self-attention, our network achieves improved performance. The coding region of the network utilizes convolutional block with large kernel convolutional attention, enabling the extraction of multi-scale features with lower computational resources while also enhancing spatial attention. The self-attention layer acts as a transition between the encoding and decoding regions, capturing long connection information and providing additional image details. To further enhance segmentation performance, we employ a multi-resolution image combination approach and adaptively extract the input form using the compression and excitation module. Additionally, we propose a novel approach that combines the principle of consistency of deep supervision mechanisms with cross-entropy and Dice loss to guide the network towards accurate segmentation. In this study, we utilized a five-fold cross-validation method to train our network model. We then performed experimental validation and evaluation on three widely-used datasets, namely REFUGE, DRISHTI-GS, and RIM-ONE-r3. Our model achieved impressive results in the cup-to-disc ratio metric, which accurately reflects the segmentation effect. Specifically, we achieved scores of 0.0242, 0.0941, and 0.0158 for the aforementioned datasets, respectively. These scores outperformed some current classical algorithms. The experimental results demonstrate that the method proposed in this paper has the capability to extract more comprehensive information about the optic cup and disc, with the ability to generalize across different datasets. Furthermore, it shows that the convolutional block with large kernel convolutional attention module can be effectively utilized for the segmentation task of optic cup and disc. These findings provide a valuable research foundation for future researchers.