Automated segmentation of key structures of the eye using a light-weight two-step classifier

Abstract

We present an automated approach to segment key structures of the eye, viz., the iris, pupil and sclera in images obtained using an Augmented Reality (AR)/ Virtual Reality (VR) application. This is done using a two-step classifier: In the first step, we use an auto encoder-decoder network to obtain a pixel-wise classification of regions that comprise the iris, sclera and the background (image pixels that are outside the region of the eye). In the second step, we perform a pixel-wise classification of the iris region to delineate the pupil. The images in the study are from the OpenEDS challenge and were used to evaluate both the accuracy and computational cost of the proposed segmentation method. Our approach achieved a score of 0.93 on the leaderboard, outperforming the baseline model by achieving a higher accuracy and using a smaller number of parameters. These results demonstrate the great promise pipelined models hold along with the benefit of using domain-specific processing and feature engineering in conjunction with deep-learning based approaches for segmentation tasks.