CNN-Based Blood Vessel Extraction from Medical Volume Data

The system performs preliminary automated extraction of blood vessel outlines using machine learning models before presenting results to operators. This preliminary action processes the bulk of extraction work automatically, allowing operators to review and correct only when necessary, thereby reducing their time burden while maintaining high accuracy through automated preprocessing

The patent replaces manual mechanical operations (operator viewing and determining outlines) with automated machine learning-based image processing systems. The CNN and other ML models automatically identify and extract blood vessel outlines from medical images, substituting human operators for automated algorithms that can process images faster and with consistent accuracy

The system dynamically adjusts multiple parameters including threshold values, model architecture configurations, and processing settings based on the specific characteristics of each medical image dataset. This adaptive parameter adjustment allows the automated system to optimize extraction accuracy for different image types and conditions without requiring manual operator intervention for each case

The patent employs a composite approach combining multiple machine learning techniques (CNN, thresholding methods, and other image processing algorithms) rather than relying on a single technique. This composite system leverages the strengths of each method to achieve high extraction accuracy while maintaining automated operation, effectively combining different computational approaches to solve the extraction problem

The system segments the blood vessel extraction process into multiple distinct stages: initial image preprocessing, automated outline extraction using machine learning, refinement of extracted features, and final validation. This segmentation allows each stage to be optimized independently for both speed and precision, with simple fast operations in early stages and more complex precise operations in later stages

The machine learning models perform preliminary feature extraction and identification of blood vessel structures before detailed outline determination. This preliminary action quickly identifies regions of interest and potential blood vessels, allowing subsequent processing to focus only on relevant areas rather than processing entire images, thereby maintaining high speed while achieving precise extraction