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Ocular Region Segmentation Model for Diagnosis of Microbial Keratitis Using Slit-Lamp Photography
(Institute of Electrical and Electronics Engineers Inc., 2023) Supreetha, R.; Sowmya Kamath, S.; Mayya, V.
Corneal disease, a prevalent cause of global blindness, can lead to severe complications such as Microbial Keratitis, an inflammatory condition of the cornea often caused by bacterial or fungal infections. Early detection and timely treatment are crucial to prevent vision loss associated with this condition. Slit-lamp photography, a standard tool for ocular examination, is commonly employed for diagnosis. To address the growing demand for ophthalmology specialists, numerous studies have explored the use of Deep Learning (DL) algorithms to achieve precise and accurate segmentation of ocular structures, including the cornea, from slit-lamp photography images. In this study, an ocular region segmentation model trained on heterogeneous slit-lamp image datasets for improving learning performance is presented. Various data augmentation strategies are experimented with, and optimization techniques are incorporated. Experiments revealed that the model outperformed several state-of-the-art works concerning Dice score. Furthermore, the model can also be utilized for the unsupervised learning task of mask generation, as the segmentation findings are on par with the ground truth. Â© 2023 IEEE.