A Hyperreflective Foci Segmentation Network for OCT Images with Multi-dimensional Semantic Enhancement

Diabetic macular edema (DME) is a leading cause of vision loss worldwide. Optical Coherence Tomography (OCT) serves as a widely accepted imaging tool for diagnosing DME due to its non-invasiveness and high resolution cross-sectional view. Clinical evaluation of Hyperreflective Foci (HRF) in OCT contributes to understanding the origins of DME and predicting disease progression or treatment efficacy. However, limited information and a significant imbalance between foreground and background in HRF present challenges for its precise segmentation in OCT images. In this study, we propose an attention mechanism-based MUlti-dimensional Semantic Enhancement Network (MUSE-Net) for HRF segmentation to address these challenges. Specifically, our MUSE-Net comprises attention-based multi-dimensional semantic information enhancement modules and class-imbalance-insensitive joint loss. The adaptive region guidance module softly allocates regional importance in slice, enriching the single-slice semantic information. The adjacent slice guidance module exploits the remote information across consecutive slices, enriching the multi-dimensional semantic information. Class-imbalance-insensitive joint loss combines pixel-level perception optimization with image-level considerations, alleviating the gradient dominance of the background during model training. Our experimental results demonstrate that MUSE-Net outperforms existing methods over two datasets respectively. To further promote the reproducible research, we made the code and these two datasets online available.