Stenosis Image Segmentation Using CPR and Volume Data Mapping
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Solution Overview
Problem
Existing imaging technologies face challenges in accurately segmenting and analyzing stenosis regions in tissues due to low contrast between the to-be-removed portions and surrounding areas, making it difficult to determine their impact on surrounding structures.
Innovation Solution
A method and system using an identification model trained on labeled training images to identify stenosis regions, combined with curved planar reformation (CPR) images and volume data mapping, to enhance segmentation and analysis accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional image segmentation methods are used, then the processing speed is fast, but the segmentation accuracy deteriorates due to low contrast between the to-be-removed portion and surrounding area
Solution Approach 1:
The patent applies preliminary action by performing image preprocessing operations (denoising, enhancement) before segmentation to improve the quality of input data. The system also performs preliminary registration of multiple imaging modalities and preliminary extraction of anatomical structures to facilitate more accurate stenosis segmentation in subsequent steps.
Solution Approach 2:
The patent uses intermediary techniques by introducing multiple imaging modalities (CT, MRI, ultrasound) as intermediate data sources to complement each other. The system also uses intermediate processing steps such as image registration, segmentation, and feature extraction as mediators to bridge the gap between raw images and final diagnosis.
2Measurement precision
If manual labeling and analysis of stenosis regions is performed, then the accuracy of identification can be high, but the time consumption and operational complexity increase significantly
Solution Approach 1:
The patent applies self-service by enabling the system to automatically perform stenosis detection, segmentation, and measurement without requiring manual intervention. The AI algorithms automatically identify stenosis regions, calculate stenosis ratios, and generate diagnostic reports, allowing the system to serve itself in the diagnostic process.
Solution Approach 2:
The patent replaces mechanical manual operations with automated computational systems. Manual labeling and measurement tasks are substituted with AI-based image analysis algorithms that automatically detect stenosis regions, compute geometric parameters, and generate diagnostic conclusions, eliminating the need for manual mechanical operations.
3Reliability
If multiple imaging modalities and complex processing steps are used, then the diagnosis accuracy improves, but the device complexity and operational difficulty increase
Solution Approach 1:
The patent applies universality by designing an integrated system that performs multiple functions (image acquisition, registration, segmentation, measurement, and diagnosis) within a single platform. The system can handle multiple imaging modalities (CT, MRI, ultrasound) and perform various diagnostic tasks, making it a universal solution that reduces operational complexity despite its comprehensive capabilities.
Data Source
AI summary
The present disclosure relates to systems and methods for image processing. The methods may include determining structure data associated with the tissue based on at least one original image of a tissue. The methods may further include determining at least one stenosis region of the tissue based on the structure data associated with the tissue. At least one of the structure data and/or the at least one stenosis region of the tissue may be configured to be displayed on a display device.