EDU-Net: Retinal Pathological Fluid Segmentation in OCT Images with Multiscale Feature Fusion and Boundary Optimization

TL;DR

EDU-Net is a novel deep learning model that combines multiscale feature fusion and boundary optimization to improve the accuracy of retinal fluid segmentation in OCT images, aiding diabetic macular edema management.

Contribution

The paper introduces EDU-Net, a dual-branch encoder-decoder network with boundary-guided attention for precise retinal fluid segmentation in OCT images.

Findings

EDU-Net outperforms existing methods in DSC segmentation performance.

The model achieves high robustness and efficiency, especially for IRF lesions.

Boundary optimization significantly improves segmentation accuracy.

Abstract

Objective: Diabetic macular edema (DME) is the leading cause of severe visual impairment in patients with diabetes. Quantification of retinal fluid, particularly intraretinal fluid (IRF) and subretinal fluid (SRF), plays a critical role in the management of DME. Although optical coherence tomography (OCT) can be used for detection, the variable morphology of fluid accumulation and the blurred boundaries caused by noise interference still limit the accuracy of OCT's automatic segmentation. Methods: Retrospective model development and validation study. This study proposes a novel edge-guided dual-branch encoder-decoder network (EDU-Net) to achieve accurate and efficient automatic segmentation of OCT liquid lesions. The local feature extraction branch is based on the EfficientNet model, which precisely captures tiny lesions by leveraging its lightweight separable convolution and high-resolution feature preservation strategy. The global feature extraction branch is based on the large-kernel efficient convolution (LKEC) module and the downsampling layer design to enhance long-range dependencies and global semantics. EDU-Net applies a multi-category edge-guided attention module to fuse high-frequency boundary detail information to each resolution feature to optimize the boundary segmentation performance. Results: Extensive results on the in-house and public datasets demonstrate that EDU-Net achieves state-of-the-art DSC segmentation performance in terms of efficiency and robustness, especially in the segmentation of IRF lesions. Conclusions: EDU-Net integrates local details with global context and optimizes boundaries, achieving an improvement in the accuracy of automatic segmentation of retinal fluid.