Automated circumpapillary retinal nerve fiber layer segmentation in high-resolution swept-source OCT

Glaucoma is a progressive optic neuropathy that leads to loss of retinal ganglion cells and thinning of retinal nerve fiber layer (RNFL). Circumpapillary RNFL thickness measurements have been used for glaucoma diagnostic and monitoring purposes. However, manual measurement of the RNFL thickness is tedious and subjective. We proposed and evaluated the performance of automated RNFL segmentation from OCT images using a state-of-the-art deep learning-based model. Circumpapillary OCT scans were extracted from volumetric OCT scans using a high-resolution swept-source OCT device. Manual annotation was performed on the extracted scans and used for training and evaluation. The results show that the accuracy and diagnostic performance is comparable to manual assessment, and the potential application of deep learning-based approach in such segmentation.