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

Objective: Hyperreflective foci (HRF) are small, punctate lesions ranging from 20 to 50 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 ranging from 20 to 50 μm with high reflective intensity in optical coherence tomography (OCT) images of patients with diabetic retinopathy (DR). This study aims to develop a model that precisely identifies and segments HRF in OCT images of DR patients. Accurate segmentation of HRF is essential for assisting ophthalmologists in early diagnosis and in assessing the effectiveness of treatment and prognosis.

Methods: We introduce an HRF segmentation algorithm based on the KiU-Net architecture. The model comprises two branches: a Kite-Net branch that uses up-sampling coding to capture detailed information, and a three-layer U-Net branch that extracts high-level semantic information. To enhance the capacity of the network, we designed a cross-attention block (CAB) that combines the information extracted from both branches, effectively integrating detail and semantic features.

Results: Experimental results demonstrate that our model significantly reduces the number of parameters while improving performance. The sensitivity (SE) and Dice Similarity Coefficient (DSC) of our model are improved to 72.90% and 66.84%, respectively. Considering the SE and precision (P) of the segmentation, as well as the recall ratio and precision of HRF detection, our model outperforms most advanced medical image segmentation algorithms CONCLUSION: The proposed HRF segmentation algorithm effectively identifies and segments HRF in OCT images of DR patients, outperforming existing methods. This advancement can significantly alleviate the burden on ophthalmologists by aiding in early diagnosis and treatment evaluation, ultimately improving patient outcomes.