Abstract

Glaucoma is a progressive optic neuropathy that is among leading causes of irreversible blindness. Structural changes in the optic nerve head identification can assist in early clinical treatment. Retinal fundus photography is a non-invasive modality for glaucoma screening, while optic disc segmentation allows measuring morphological properties of clinical relevance. There are numerous deep learning algorithms developed for optic disc segmentation and glaucoma diagnosis; these tasks are often treated independently from each other. While convolutional architectures allow capturing the local spatial features effectively, they can experience challenges in capturing long-range dependencies and complex anatomy. Traditional segmentation networks can also suffer performance drop due to ambiguous, under-illuminated, occluded by blood vessels, or affected by pathology borders of the optic disc. This paper suggests Attention-Driven Vision Capsule Transformer (AVCT-Net) approach for simultaneous optic disc segmentation and glaucoma diagnosis. The approach involves hierarchical convolutional feature extraction, multi-scale attention mechanism, vision transformer-based global contextual analysis, and capsule-based feature representation. While the attention mechanism allows highlighting the most discriminating optic nerve areas, the transformer captures the long-range relationships between different retinal areas. Capsules provide preserving spatial and hierarchical dependencies between optic disc features and glaucoma-specific morphology. A multi-task learning approach is used for simultaneous optimization of segmentation and classification loss functions, thus, allowing utilizing anatomically relevant features generated by the segmentation branch in the diagnostic branch. According to experiments conducted on the REFUGE dataset, the proposed AVCT-Net algorithm allows achieving 98.5% accuracy, 97.7% precision, 98.2% recall, and 97.9% F1-score for glaucoma classification, as well as 98.4% Dice coefficient for optic disc segmentation within 100 training epochs. Compared with M-Net, Attention U-Net, and SwinCup-DiscNet approaches, the suggested approach allows improving accuracy by 2.7, 2.0, and 0.7%, respectively. The improvements in precision are 2.5, 1.8, and 0.6 points, while recall improvements are 2.1, 1.2, and 0.6 points, respectively. The improvements in F1-score are 2.3, 1.5, and 0.5 points, correspondingly. In optic disc segmentation task, AVCT-Net approach reaches 98.4% Dice coefficient, which is better than the results achieved by M-Net (by 2.1 points), Attention U-Net (by 1.4 points), and SwinCup-DiscNet (by 0.3 points).

Authors

Utipmfon Sukmama Jimmy
Concordia University of Edmonton, Canada

Keywords

Glaucoma Diagnosis, Optic Disc Segmentation, Vision Transformer, Capsule Network, Attention Mechanism