A Novel Intraretinal Layer Semantic Segmentation Method of Fundus OCT Images Based on the TransUNet Network Model

Abstract

Optical coherence tomography (OCT) is used to obtain retinal images and stratify them to obtain the thickness of each intraretinal layer, which plays an important role in the clinical diagnosis of many ophthalmic diseases. In order to overcome the difficulties of layer segmentation caused by uneven distribution of retinal pixels, fuzzy boundaries, unclear texture, and irregular lesion structure, a novel lightweight TransUNet deep network model was proposed for automatic semantic segmentation of intraretinal layers in OCT images. First, ResLinear-Transformer was introduced into TransUNet to replace Transformer in TransUNet, which can enhance the receptive field and improve the local segmentation effect. Second, Dense Block was used as the decoder of TransUNet, which can strengthen feature reuse through dense connections, reduce feature parameter learning, and improve network computing efficiency. Finally, the proposed method was compared with the state-of-the-art on the public SD-OCT dataset of diabetic macular edema (DME) patients released by Duke University and POne dataset. The proposed method not only improves the overall semantic segmentation accuracy of retinal layer segmentation, but also reduces the amount of computation, achieves better effect on the intraretinal layer segmentation, and can better assist ophthalmologists in clinical diagnosis of patients.