Joint Segmentation of Multi-Class Hyper-Reflective Foci in Retinal Optical Coherence Tomography Images.

Hyper-reflective foci (HRF) refers to the spot-shaped, block-shaped areas with characteristics of high local contrast and high reflectivity, which is mostly observed in retinal optical coherence tomography (OCT) images of patients with fundus diseases. HRF mainly appears hard exudates (HE) and microglia (MG) clinically. Accurate segmentation of HE and MG is essential to alleviate the harm in retinal diseases. However, it is still a challenge to segment HE and MG simultaneously due to similar pathological features, various shapes and location distribution, blurred boundaries, and small morphology dimensions. To tackle these problems, in this paper, we propose a novel global information fusion and dual decoder collaboration-based network (GD-Net), which can segment HE and MG in OCT images jointly. Specifically, to suppress the interference of similar pathological features, a novel global information fusion (GIF) module is proposed, which can aggregate the global semantic information efficiently. To further improve the segmentation performance, we design a dual decoder collaborative workspace (DDCW) to comprehensively utilize the semantic correlation between HE and MG while enhancing the mutual influence on them by feedback alternately. To further optimize GD-Net, we explore a joint loss function which integrates pixel-level with image-level. The dataset of this study comes from patients diagnosed with diabetic macular edema at the department of ophthalmology, University Medical Center Groningen, The Netherlands. Experimental results show that our proposed method performs better than other state-of-the-art methods, which suggests the effectiveness of the proposed method and provides research ideas for medical applications.

Algorithm for Detection and Quantification of Hyperreflective Dots on Optical Coherence Tomography in Diabetic Macular Edema.

Purpose: To develop an algorithm to detect and quantify hyperreflective dots (HRDs) on optical coherence tomography (OCT) in patients with diabetic macular edema (DME). Materials and Methods: Twenty OCTs (each OCT contains 128 b scans) from 20 patients diagnosed with DME were included in this study. Two types of HRDs, hard exudates and small HRDs (hypothesized to be activated microglia), were identified and labeled independently by two raters. An algorithm using deep learning technology was developed based on input (in total 2,560 OCT b scans) of manual labeling and differentiation of HRDs from rater 1. 4-fold cross-validation was used to train and validate the algorithm. Dice coefficient, intraclass coefficient (ICC), correlation coefficient, and Bland-Altman plot were used to evaluate agreement of the output parameters between two methods (either between two raters or between one rater and proposed algorithm). Results: The Dice coefficients of total HRDs, hard exudates, and small HRDs area of the algorithm were 0.70 ± 0.10, 0.72 ± 0.11, and 0.46 ± 0.06, respectively. The correlations between rater 1 and proposed algorithm (range: 0.95-0.99, all p < 0.001) were stronger than the correlations between the two raters (range: 0.84-0.96, all p < 0.001) for all parameters. The ICCs were higher for all the parameters between rater 1 and proposed algorithm (range: 0.972-0.997) than those between the two raters (range: 0.860-0.953). Conclusions: Our proposed algorithm is a good tool to detect and quantify HRDs and can provide objective and repeatable information of OCT for DME patients in clinical practice and studies.