Optic Disc Segmentation: From Classical Methods to AI in Retinal Analysis

This review paper details the evolution of optic disc segmentation techniques in fundus images, a crucial step for diagnosing conditions like glaucoma. It traces the progression from traditional image processing and deformable models to modern AI-driven methods, including deep learning and Transformer-based approaches. The paper highlights persistent challenges such as boundary ambiguity and domain generalization, while noting the enduring importance of principles like region localization and geometric constraints. AI

IMPACT Provides a comprehensive overview of AI's role in advancing medical image analysis for diagnosing eye conditions.

RANK_REASON The item is a review paper published on arXiv discussing a research topic. [lever_c_demoted from research: ic=1 ai=1.0]

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• alphaXiv
• Deformable Models for Segmentation of Medical Ultrasound Images
• Fundus images analysis using deep features for detection of exudates, hemorrhages and microaneurysms
• glaucoma
• Hugging Face
• image processing
• Level-set models
• optic disc
• Transformer++

• paper
• other

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