Joint Segmentation and Grading with Iterative Optimization for Multimodal Glaucoma Diagnosis
This work addresses early-stage glaucoma diagnosis for medical imaging applications, representing an incremental improvement through multimodal integration.
The paper tackles the challenge of accurate glaucoma diagnosis by proposing an iterative multimodal optimization model that jointly performs segmentation and grading using both fundus and OCT images, achieving comprehensive assessment with clinically significant results.
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.