Vessel Structure Extraction using Constrained Minimal Path Propagation.

Area of Science:

• Medical Imaging Analysis
• Computational Anatomy
• Image Processing

Background:

• The minimal path method is effective for extracting vascular structures in medical imaging.
• Previous work introduced minimal path propagation with backtracking (MPP-BT) for curve-like structures like vessel centerlines.
• Challenges remain in extracting complete vessel lumens and handling missing structures due to closed loops.

Purpose of the Study:

• To propose a robust constrained minimal path propagation (CMPP) method for enhanced vascular structure extraction.
• To extend minimal path propagation techniques for complete vessel lumen extraction and improved handling of complex vascular geometries.
• To develop a fast, unsupervised method requiring minimal user input for comprehensive vascular segmentation.

Main Methods:

• Introduced CMPP, utilizing a secondary minimal path propagation procedure for vessel lumen extraction post-centerline identification.
• Implemented a local MPP-BT process to address structure under-segmentation caused by closed-loop artifacts.
• Validated the unsupervised approach on diverse datasets including 2D cardiac angiography, 2D retinal images, and 3D CT angiography, requiring only a single starting point.

Main Results:

• Achieved high performance on retinal images with specificity (Sp) of 0.9750 and sensitivity (Se) of 0.6591.
• Demonstrated excellent results on 3D synthetic vascular datasets with specificity (Sp) and sensitivity (Se) exceeding 0.99.
• Analyzed parameter settings and computational costs, confirming the method's efficiency.

Conclusions:

• The proposed CMPP method offers a robust, fast, and unsupervised solution for extracting complete vascular structures from various medical imaging modalities.
• CMPP effectively extracts vessel centerlines and lumens while robustly handling complex anatomical challenges like closed loops.
• The method's high specificity and sensitivity across different datasets highlight its potential for clinical applications in vascular analysis.