Automatic segmentation of hyperreflective dots via focal priors and visual saliency.

Purpose: Hyperreflective dots (HRDs) can be observed in spectral domain optical coherence tomography (SD-OCT), which can provide a sensitive marker in the treatment decision process. Quantitative analyses of HRDs are the key to make appropriate decisions on observation, treatment, and retreatment. The purpose of this study is to automatically and accurately segment HRDs in SD-OCT B-scans with diabetic retinopathy (DR).

Methods: The authors propose an automatic segmentation algorithm of HRDs via focal priors and visual saliency. The algorithm is divided into three stages: segmentation of retinal layers, calculation of the multiscale local contrast saliency map, and adaptive threshold segmentation. First, a method based on improved graph search is used to segment retinal layers to obtain the region of interest (ROI) and the reflectivity estimation of the retinal pigment epithelium (RPE) layer; then, the multiscale local contrast saliency map is obtained by using a local contrast measure, which measures the dissimilarity between the current pixels and corresponding neighborhoods; finally, an adaptive threshold is applied to segment HRDs.

Results: Experimental results on 20 SD-OCT B-scans demonstrate that our method is effective for HRDs segmentation. The average dice similarity coefficient (DSC) and detection accuracy are 71.12% and 85.07%, respectively.

Conclusions: The proposed method can accurately segment HRDs in SD-OCT B-scans with DR and outperforms current state-of-the-art methods. Our method can provide reliable HRDs segmentation to assist ophthalmologists in clinical diagnosis, treatment, disease monitoring, and progression.