Research on Semantic Segmentation Method of Macular Edema in Retinal OCT Images Based on Improved Swin-Unet

Abstract

Optical coherence tomography (OCT), as a new type of tomography technology, has the characteristics of non-invasive, real-time imaging and high sensitivity, and is currently an important medical imaging tool to assist ophthalmologists in the screening, diagnosis, and follow-up treatment of patients with macular disease. In order to solve the problem of irregular occurrence area of diabetic retinopathy macular edema (DME), multi-scale and multi-region cluster of macular edema, which leads to inaccurate segmentation of the edema area, an improved Swin-Unet networks model was proposed for automatic semantic segmentation of macular edema lesion areas in OCT images. Firstly, in the deep bottleneck of the Swin-Unet network, the Resnet network layer was used to increase the extraction of pairs of sub-feature images. Secondly, the Swin Transformer block and skip connection structure were used for global and local learning, and the regions after semantic segmentation were morphologically smoothed and post-processed. Finally, the proposed method was performed on the macular edema patient dataset publicly available at Duke University, and was compared with previous segmentation methods. The experimental results show that the proposed method can not only improve the overall semantic segmentation accuracy of retinal macular edema, but also further to improve the semantic segmentation effect of multi-scale and multi-region edema regions.

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“…Kim et al [11] incorporated audiovisual datasets into a facial expression recognition algorithm based on Swin Transformer and demonstrated the effectiveness of multimodality. Gao and Chen [12] employed the Swin-UNET network, leveraging the ResNet network layer to enhance sub-feature image extraction. By combining the Swin Transformer block and skip connection structure for global and local learning, they achieved improved semantic segmentation accuracy for macular edema.…”