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

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.