ViT-Pupil: Vision Transformer-Based Segmentation of Pupil Images for Detection of Computer Vision Syndrome

Computer vision syndrome (CVS) is a major illness derived as a result of long-term improper usage of digital devices. It causes ocular as well as extraocular symptoms that may lead to blindness and cancers in severe cases. Therefore, early detection and diagnosis are crucial for preventing vision loss. Pupillometry offers an objective assessment of CVS, as continuous light exposure leads to characteristic changes in pupil size. In this paper, a transformer model algorithm for the segmentation of pupil, “ViT-Pupil” is employed as a preliminary step to evaluate pupil changes. This proposed method uses a CASIA iris degradation database for training the model, which contains gray-scale eye images that are of different sizes, shapes, and occlusions due to eyelashes. The model resulted in a validation dice coefficient of 0.93. A cross-domain generalization technique is carried out on a customized dataset containing RGB images captured using a low-resolution mobile camera under different light intensity levels using the proposed model. The customized images undergo preprocessing as an initial step. The segmentation result for the customized dataset indicates the superior performance of ViT-Pupil for a noisy, unclear images, which shows the robustness of the proposed method. Thus, the proposed method can be utilized to track the pupillary changes by integrating with wearable devices as a preliminary step for the identification of CVS.