Joint Segmentation of Multi-Class Hyper-Reflective Foci in Retinal Optical Coherence Tomography Images

Hyper-reflective foci (HRF) refers to the spot-shaped, block-shaped areas with characteristics of high local contrast and high reflectivity, which is mostly observed in retinal optical coherence tomography (OCT) images of patients with fundus diseases. HRF mainly appears hard exudates (HE) and microglia (MG) clinically. Accurate segmentation of HE and MG is essential to alleviate the harm in retinal diseases. However, it is still a challenge to segment HE and MG simultaneously due to similar pathological features, various shapes and location distribution, blurred boundaries, and small morphology dimensions. To tackle these problems, in this paper, we propose a novel global information fusion and dual decoder collaboration-based network (GD-Net), which can segment HE and MG in OCT images jointly. Specifically, to suppress the interference of similar pathological features, a novel global information fusion (GIF) module is proposed, which can aggregate the global semantic information efficiently. To further improve the segmentation performance, we design a dual decoder collaborative workspace (DDCW) to comprehensively utilize the semantic correlation between HE and MG while enhancing the mutual influence on them by feedback alternately. To further optimize GD-Net, we explore a joint loss function which integrates pixel-level with image-level. The dataset of this study comes from patients diagnosed with diabetic macular edema at the department of ophthalmology, University Medical Center Groningen, The Netherlands. Experimental results show that our proposed method performs better than other state-of-the-art methods, which suggests the effectiveness of the proposed method and provides research ideas for medical applications.