Vascular Centerline Extraction via Fragmented Image Segmentation
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Solution Overview
Problem
Existing methods for extracting vascular centerlines in medical images are cumbersome, inefficient, and difficult to repeat, particularly in the diagnosis and treatment of vascular diseases like atherosclerosis, as they rely heavily on manual labor and fail to provide accurate representation of vascular walls and plaques.
Innovation Solution
A method and system for vascular image processing that includes generating vascular fragment images and centerline images using machine learning models, such as vascular fragmentation and segmentation models, to automate the extraction of vascular centerlines and three-dimensional vascular wall images from medical images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual methods are used to extract vascular centerlines, then the process can be performed with simple equipment, but the extraction accuracy is low and the process is cumbersome
Solution Approach 1:
The patent segments the vascular image processing into distinct stages: vascular segmentation to obtain binary vascular images, vascular fragmentation to divide continuous vessels into segments, and centerline extraction for each segment. This segmentation approach improves extraction accuracy by handling complex vascular structures in manageable parts while maintaining systematic processing.
Solution Approach 2:
The patent introduces intermediate processing steps between raw image input and final centerline output. Vascular segmentation creates binary images as intermediaries, and vascular fragmentation creates segment images as intermediaries. These intermediary representations facilitate more accurate centerline extraction while providing structured processing that manages system complexity.
2Productivity
If manual extraction methods are used, then the equipment requirements are simple, but the processing efficiency is low and difficult to repeat
Solution Approach 1:
The patent replaces manual mechanical extraction methods with automated image processing algorithms. Vascular segmentation models and fragmentation algorithms automatically process medical images to extract centerlines, eliminating manual intervention. This substitution dramatically improves processing efficiency and repeatability while requiring sophisticated software systems.
Solution Approach 2:
The patent employs multiple processing parameters and models that can be adjusted and optimized. Vascular segmentation uses adjustable threshold parameters, fragmentation uses configurable segment criteria, and centerline extraction uses tunable algorithm parameters. This parametric approach enables efficient automated processing while allowing adaptation to different vascular structures and imaging conditions.
3Manufacturing precision
If simple processing methods are used, then the system is easy to operate, but the representation of vascular walls and plaques is inaccurate
Solution Approach 1:
The patent segments vascular structures into distinct components: vascular walls, lumens, and plaques are separately identified and represented. Vascular fragmentation divides the vessel into segments that can be individually analyzed. This segmentation enables precise representation of complex vascular features while maintaining systematic processing that simplifies operation.
Solution Approach 2:
The patent applies different processing qualities to different parts of the vascular structure. Vascular walls receive specialized segmentation treatment, plaques receive distinct identification, and different vessel segments can be processed with appropriate local parameters. This local quality approach achieves high representation accuracy for each vascular component while maintaining unified system operation.
Data Source
AI summary
The present disclosure relates to methods and systems for vascular image processing. The method may include obtaining an initial vascular image, generating a vascular fragment image by performing a vascular fragmentation operation on the initial vascular image, and generating, based on the vascular fragment image, a vascular centerline image.