MT_Net: A Multi-Scale Framework Using the Transformer Block for Retina Layer Segmentation

Abstract

Variations in the thickness of retinal layers serve as early diagnostic indicators for various fundus diseases, and precise segmentation of these layers is essential for accurately measuring their thickness. Optical Coherence Tomography (OCT) is an important non-invasive tool for diagnosing various eye diseases through the acquisition and layering of retinal images. However, noise and artifacts in images present significant challenges in accurately segmenting retinal layers. We propose a novel method for retinal layer segmentation that addresses these issues. This method utilizes ConvNeXt as the backbone network to enhance multi-scale feature extraction and incorporates a Transformer–CNN module to improve global processing capabilities. This method has achieved the highest segmentation accuracy on the Retina500 dataset, with a mean Intersection over Union (mIoU) of 81.26% and an accuracy (Acc) of 91.38%, and has shown excellent results on the public NR206 dataset.

Keywords

• retina layer
• segmentation
• OCT
• transformer
• MT_Net