Segmentation and Classification Approaches of Clinically Relevant Curvilinear Structures: A Review.

Area of Science:

• Medical Imaging Analysis
• Computer Vision
• Computational Biology

Background:

• Automated detection of curvilinear structures (e.g., fungal hyphae, corneal nerves, retinal vessels) is crucial for clinical diagnosis.
• Variations in size and appearance of these structures present significant challenges for traditional methods.
• Deep learning methods offer superior self-learning and feature extraction capabilities for complex image analysis.

Purpose of the Study:

• To review and summarize traditional and deep learning methods for detecting curvilinear structures in microscopic images.
• To identify novel techniques and cross-domain adaptations for improved segmentation and classification.
• To address challenges like thin structures, bifurcations, artifacts, and complex backgrounds.

Main Methods:

• Comprehensive literature review of methods published between 2015 and 2021.
• Focus on techniques applied to retinal vessels, corneal nerves, and filamentous fungi.
• Analysis of approaches addressing image artifacts and challenging backgrounds.

Main Results:

• Deep learning methods demonstrate superior performance in automated feature learning and generalization.
• Successful applications in detecting diabetic neuropathic complications via corneal nerve analysis.
• Novel techniques for retinal vessel segmentation and classification show promise for cross-domain adaptation.

Conclusions:

• Deep learning significantly advances the automated detection of clinically relevant curvilinear structures.
• Cross-domain adaptation of successful retinal vessel segmentation techniques can benefit corneal and fungal analyses.
• Further research can leverage these methods for more accurate and efficient clinical diagnosis.