Semi-supervised learning approach for automatic detection of hyperreflective foci in SD-OCT imaging

Purpose: This work investigates a semi-supervised approach for automatic detection of hyperreflective foci (HRF) in spectral-domain optical coherence tomography (SD-OCT) imaging. Starting with a limited annotated data set containing HRFs, we aim to build a larger data set and then a more robust detection model. Methods: Faster RCNN model for object detection was trained in a semi-supervised manner whereby high confidence detections from the current iteration are added to the training set in subsequent iterations after manual verification. With each iteration the size of the training set is increased by including model detected additional cases. We expect the model to be more accurate and robust as the number of training iterations increase. We performed experiments in a data set consisting over 170,000 SD-OCT B scans. The models were tested in a data set consisting of 30 patients (3630 B scans). Results: Across iterations the model performance improved with final model yielding precision=0.56, recall=0.99, and F1-score=0.71. As the number of training example increases the model detects cases with more confidence. The high false positive rate is associated with additional detections that capture instances of elevated reflectivity which upon review were found to represent questionable cases rather than definitive HRFs due to confounding factors. Conclusion: We demonstrate that by starting with a small data set of HRFs we are able to search the occurrences of other HRFs in the data set in a semi-supervised fashion. This method provides an objective, time, and cost-effective alternative to laborious manual inspection of B-scans for HRF occurrences.