Blood Vessel Segmentation Using Multi-Channel Black Blood MRI
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current medical imaging techniques for cerebrovascular diseases are inefficient and inaccurate, particularly in the automated assessment of vessel segmentation and detection of vascular abnormalities, which hinders timely and precise diagnosis and treatment.
Innovation Solution
A system and method utilizing multiple imaging modalities, including black blood MRI, to generate and process images using a blood vessel segmentation model and registration techniques, enabling automated detection of vascular structures and abnormalities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional medical imaging techniques are used for cerebrovascular disease diagnosis, then the imaging process can be performed with conventional equipment, but the processing efficiency and accuracy are insufficient
Solution Approach 1:
The patent replaces traditional manual image processing methods with an automated machine learning-based system. A blood vessel segmentation model processes medical images to automatically identify and segment blood vessels, replacing manual analysis with computational algorithms that achieve both high efficiency and accuracy in vascular detection
Solution Approach 2:
The patent transforms image processing from conventional methods to deep learning-based methods by changing the fundamental parameters of the processing system. The blood vessel segmentation model uses neural network parameters to extract vascular information, enabling simultaneous improvement in processing speed and detection precision
2Loss of information
If single-modality imaging is used, then the imaging process is simple and quick, but comprehensive vascular information including both walls and lumens cannot be obtained
Solution Approach 1:
The patent merges multiple imaging modalities (black blood images showing vascular walls and bright blood images showing vascular lumens) into a unified processing framework. The blood vessel segmentation model integrates information from both modalities to produce comprehensive vascular segmentation results, eliminating information loss while managing system complexity through integrated processing
3Productivity
If manual blood vessel segmentation is performed, then the process is flexible and adaptable, but the processing time is long and productivity is low
Solution Approach 1:
The blood vessel segmentation model performs self-service by automatically processing and segmenting blood vessels in medical images without requiring manual intervention. The trained neural network independently analyzes images, identifies vascular structures, and generates segmentation results, dramatically increasing processing throughput while maintaining operational simplicity through automated execution
Data Source
Figure 1
Figure 2
Figure 3
AI summary
The present disclosure provides methods and systems for image processing. The methods may include obtaining multiple black blood images of a first region of a target subject. The methods may further include generating a first segment result by inputting the black blood images into multiple input channels of a blood vessel segmentation model. The first segment result may include blood vessel segments of first blood vessels in the first region, the blood vessel segmentation model may be a trained machine learning model, and each of the black blood images may be randomly designated as an input image of one of the input channels.