Joint Segmentation and Grading with Iterative Optimization for Multimodal Glaucoma Diagnosis

TL;DR

This paper introduces an iterative multimodal optimization model that combines fundus and OCT data with advanced feature alignment and refinement techniques to improve glaucoma diagnosis accuracy.

Contribution

It presents a novel joint segmentation and grading framework using mid-level fusion and diffusion-based refinement for multimodal glaucoma assessment.

Findings

Effective multimodal feature integration demonstrated

Improved segmentation of optic disc and cup

Accurate glaucoma grading achieved

Abstract

Accurate diagnosis of glaucoma is challenging, as early-stage changes are subtle and often lack clear structural or appearance cues. Most existing approaches rely on a single modality, such as fundus or optical coherence tomography (OCT), capturing only partial pathological information and often missing early disease progression. In this paper, we propose an iterative multimodal optimization model (IMO) for joint segmentation and grading. IMO integrates fundus and OCT features through a mid-level fusion strategy, enhanced by a cross-modal feature alignment (CMFA) module to reduce modality discrepancies. An iterative refinement decoder progressively optimizes the multimodal features through a denoising diffusion mechanism, enabling fine-grained segmentation of the optic disc and cup while supporting accurate glaucoma grading. Extensive experiments show that our method effectively integrates multimodal features, providing a comprehensive and clinically significant approach to glaucoma assessment. Source codes are available at https://github.com/warren-wzw/IMO.git.