Macular Holes Detection Using Deep Learning on Optical Coherence Tomography Images

Macular holes (MHs) can be either idiopathic or secondary to a result of concurrent or previous pathology such as ocular inflammation, trauma or surgery. Idiopathic macular holes may eventually impair the life quality and self-care capability of patients. In clinical, the optical coherence tomography (OCT) images of macular holes can be divided into 4 stages based upon the size of macular hole. The size of macular hole is inversely influencing surgical success rate and visual outcomes. Minimizing human judgment errors in the classification of MHs and enhancing the overall quality of classification are critical objectives. In this paper, we develop a deep learning algorithm to detect MHs on OCT images and also propose an automatic algorithm to measure the size of MH. The MH detection algorithm demonstrates exceptional accuracy, while the measurement algorithm offers superior efficiency when compared to the conventional caliper-based method utilized with spectral-domain OCT devices - a time-consuming procedure for ophthalmologists. These advancements promise to expedite the diagnostic process and facilitate to rapidly recognize the stage 2 MHs from stage 3 or 4 cases.