Gap-free segmentation of vascular networks with automatic image processing pipeline

Area of Science:

• Medical Imaging
• Image Processing
• Computational Biology

Background:

• Current image processing methods struggle with vascular tree connectivity, especially in small vessels and bifurcations.
• Imaging artifacts and noise disrupt segmentation, hindering topological analysis and visualization.
• Accurate vascular network representation is vital for disease diagnosis and patient-specific simulations.

Purpose of the Study:

• To present a fully automated vessel enhancement pipeline for improved segmentation of tree-like vascular structures.
• To enable robust topological and statistical analysis of cerebral angioarchitecture.
• To reduce operator dependency and time constraints associated with manual segmentation.

Main Methods:

• Developed a fully automated vessel enhancement pipeline with automated parameter settings.
• Applied the pipeline to various imaging sources: 3D rotational angiography, MR angiography, MR venography, and CT angiography.
• The output is a vessel-enhanced image suitable for generating anatomical network representations.

Main Results:

• The pipeline reliably enhances vessel structures, preserving connectivity in bifurcations and small vessels.
• Generated anatomically consistent network representations of cerebral angioarchitecture.
• The method provides a foundation for computer-aided diagnosis and epidemiological analysis of cerebrovascular diseases.

Conclusions:

• The automated vessel enhancement pipeline offers a robust solution for accurate vascular tree segmentation.
• This technique facilitates topological and statistical analysis, improving cerebrovascular disease diagnosis.
• It reduces manual segmentation efforts, enabling large-scale clinical data analysis and patient-specific simulations.