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 MUSENet
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-imbalanceinsensitive
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.