Advances in MRI optic nerve segmentation.

Area of Science:

• Neuroimaging
• Medical Image Analysis
• Neuroscience

Background:

• Optic nerve damage, often seen in multiple sclerosis via optic neuritis, is increasingly studied using advanced MRI.
• Advances in MRI technology and deep learning have enhanced optic nerve visualization and analysis.
• Accurate optic nerve segmentation is vital for early disease diagnosis, treatment planning, and radiotherapy.

Purpose of the Study:

• To review the evolution of optic nerve MRI segmentation techniques.
• To highlight the transition from traditional methods to deep learning approaches.
• To assess the impact of these advancements on clinical applications.

Main Methods:

• Systematic review of 27 peer-reviewed articles published between 2007 and 2024.
• Analysis of segmentation methods, from intensity-based to deep learning and multi-atlas techniques.
• Examination of studies utilizing single or multiple MRI modalities.

Main Results:

• Optic nerve segmentation has progressed from basic intensity-based methods to complex deep learning algorithms.
• Deep learning and multi-atlas methods show improved accuracy in detecting subtle optic nerve changes.
• The review covers a decade of advancements in optic nerve imaging analysis.

Conclusions:

• Optic nerve MRI segmentation has undergone significant evolution, driven by technological and algorithmic innovation.
• Advanced segmentation techniques, particularly deep learning, are crucial for improving diagnostic accuracy and treatment strategies for optic nerve disorders.
• Continued research in this area promises further improvements in managing neurodegenerative diseases affecting the optic nerve.