Enhancing diabetic retinopathy diagnosis: automatic segmentation of hyperreflective foci in OCT via deep learning

作者：Yixiao Li, Boyu Yu, Mingwei Si, Mengyao Yang, Wenxuan Cui, Yi Zhou, Shujun Fu, Hong Wang, Xuya Liu, Han Zhang · 发表于：International Ophthalmology · 年份：2025 · DOI：10.1007/s10792-025-03439-z · 被引用次数：6 · 研究领域：Retinal Imaging and Analysis、Retinal Diseases and Treatments、AI in cancer detection

Abstract Objective Hyperreflective foci (HRF) are small, punctate lesions ranging from 20 to 50 $$\mu$$ μ m and exhibiting high reflective intensity in optical coherence tomography (OCT) images of patients with diabetic retinopathy (DR). The purpose of the model proposed in this paper is to precisely identify and segment the HRF in OCT images of patients with DR. This method is essential for assisting ophthalmologists in the early diagnosis and assessing the effectiveness of treatment and prognosis. In this study, we introduce an HRF segmentation algorithm based on KiU-Net, the algorithm that comprises the Kite-Net branch using up-sampling coding to collect more detailed information and a three-layer U-Net branch to extract high-level semantic information. To enhance the capacity of a single-branch network, we also design a cross-attention block (CAB) which combines the information extracted from two branches. The experimental results demonstrate that the number of parameters of our model is significantly reduced, and the sensitivity (SE) and the dice similarity coefficient (DSC) are respectively improved to 72.90 $$\%$$ % and 66.84 $$\%$$ % . Considering the SE and precision(P) of the segmentation, as well as the recall ratio and recall P of HRF, we believe that this model outperforms most advanced medical image segmentation algorithms and significantly relieves the strain on ophthalmologists. Purpose Hyperreflective foci (HRF) are small, punctate lesions r...