An Update on Choroidal Layer Segmentation Methods in Optical Coherence Tomography Images: a Review

Area of Science:

• Ophthalmology
• Medical Imaging
• Biomedical Engineering

Background:

• The choroid, a key eye layer between the sclera and retina, is crucial for diagnosing pathologies like choroidal tumors.
• Accurate choroidal segmentation aids in identifying conditions such as polypoidal choroidal vasculopathy.
• Spectral-Domain Optical Coherence Tomography (SD-OCT) provides high-quality imaging for choroidal visualization.

Purpose of the Study:

• To conduct a comprehensive review of recently published automatic choroidal segmentation algorithms.
• To address the limitations of manual choroidal segmentation, including time consumption, tedium, and human error.
• To explore methods that account for variables affecting choroidal thickness, such as axial length (AXL), time of day, and age.

Main Methods:

• Systematic literature review of automatic choroidal segmentation techniques.
• Analysis of algorithms applied to Spectral-Domain Optical Coherence Tomography (SD-OCT) data.
• Evaluation of methods considering physiological variables impacting choroidal segmentation.

Main Results:

• Identified various recently developed automatic choroidal segmentation algorithms.
• Highlighted the advantages of automated methods over manual segmentation in terms of efficiency and accuracy.
• Discussed the importance of incorporating factors like axial length (AXL) and age in segmentation models.

Conclusions:

• Automatic choroidal segmentation methods offer a promising solution to the challenges of manual segmentation in ophthalmology.
• Further research into robust algorithms is essential for accurate diagnosis and management of eye diseases.
• SD-OCT combined with advanced segmentation techniques can significantly improve clinical practice.