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Multimodality analysis of Hyper-reflective Foci and Hard Exudates in Patients with Diabetic Retinopathy.

Sijie NiuChenchen YuQiang ChenSongtao YuanJiang LinWen FanQinghuai Liu
Published in: Scientific reports (2017)
To investigate the correlations between hyper-reflective foci and hard exudates in patients with non-proliferative diabetic retinopathy (NPDR) and proliferative diabetic retinopathy (PDR) by spectral-domain optical coherence tomography (SD OCT) images. Hyper-reflective foci in retinal SD OCT images were automatically detected by the developed algorithm. Then, the cropped CFP images generated by the semi-automatic registration method were automatically segmented for the hard exudates and corrected by the experienced clinical ophthalmologist. Finally, a set of 5 quantitative imaging features were automatically extracted from SD OCT images, which were used for investigating the correlations of hyper-reflective foci and hard exudates and predicting the severity of diabetic retinopathy. Experimental results demonstrated the positive correlations in area and amount between hard exudates and hyper-reflective foci at different stages of diabetic retinopathy, with statistical significance (all p < 0.05). In addition, the area and amount can be taken as potential discriminant indicators of the severity of diabetic retinopathy.
Keyphrases
  • diabetic retinopathy
  • optical coherence tomography
  • deep learning
  • optic nerve
  • machine learning
  • convolutional neural network
  • risk assessment
  • computed tomography
  • human health