Purpose: Diabetic macular edema (DME) is a retinal disorder characterized by a buildup of cystoidal fluid in the retina.
The typical treatment consists of monthly intravitreal anti vascular endothelial growth factor (anti-VEGF) injections.
However, the efficacy of this treatment varies strongly.
Recent studies have indicated that the presence and number of hyperreflective foci can possibly be considered a prognostic biomarker for treatment response in DME.
As the detection of foci is difficult and time-consuming manual foci quantification seems infeasible.
We therefore developed a fully automated system capable of detecting and quantifying foci in optical coherence tomography (OCT) images.
Methods:
119 fovea centered B-scans obtained from 49 patients with DME were selected from a clinical database.
The data was divided in a training set of 96 B-scans from 40 patients, and a test set containing 23 B-scans from 9 patients.
A convolutional neural network (CNN) was developed to predict if an image pixel belongs to a hyperreflective focus by considering a small neighborhood around the pixel of interest.
The CNN consists of 7 convolutional layers and 2 max pooling layers.
After providing the system with enough training samples, the network automatically detects pixels with a high probability of being part of a hyperreflective focus.
Connected detections are considered as a single detection.
The obtained results were compared to manual annotations made by two experienced human graders in consensus for the central 3 mm surrounding the fovea.
Hyperreflective foci were only annotated in the layers ranging from the inner plexiform layer (IPL) to the outer nuclear layer (ONL) as manual detection is challenging in the other layers.
When a detection is overlapping with an annotated focus it is considered a true positive, otherwise it is counted as a false positive.
Results:
In the independent test set a sensitivity of 0.83 was obtained.
At this level of sensitivity, an average of 8.3 false positives per B-scan were detected.
False positives were mainly caused by detections outside the selected range (ILP to ONL) and misdetections by the graders.
Conclusions:
An image analysis algorithm for the automatic detection and quantification of hyperreflective foci in OCT B-scans was developed.
The experiments show promising results to obtain quantitative foci based biomarkers that can be used for the prediction of treatment response in DME.