Cardiac Chamber Segmentation via Multi-Level Margin Adjustment
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Solution Overview
Problem
Current image segmentation techniques face challenges in accurately segmenting multiple cardiac chambers in a single image, as they often interact with each other, making it difficult to distinguish and isolate each chamber effectively.
Innovation Solution
An image processing method and system that acquires a region of interest (ROI) with multiple margins, generates a first model based on these margins, adjusts a sub-model within the ROI using correlation factors and energy functions, and employs a classifier to determine target points for precise segmentation, allowing for the segmentation of cardiac chambers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a deformable model is used for segmenting cardiac chambers, then the segmentation can be achieved based on matching the model with the reconstructed image, but it becomes difficult to segment each cardiac chamber when multiple chambers are present in a single image due to mutual effects between chambers
Solution Approach 1:
The patent divides the segmentation process into multiple stages: first segmenting the entire ROI containing all cardiac chambers, then identifying subregions corresponding to individual chambers, and finally performing refined segmentation on each subregion. This multi-level segmentation approach isolates the mutual effects between chambers by processing them separately after initial grouping, thereby maintaining segmentation accuracy while managing complexity.
Solution Approach 2:
The patent implements a nested segmentation structure where the ROI is first segmented as a whole, then subregions within the ROI are identified, and finally individual cardiac chambers are segmented within each subregion. This nested approach allows the segmentation process to operate at multiple scales, with each level providing context for the next, effectively handling multiple chambers by nesting them within hierarchical regions.
2Reliability
If multiple cardiac chambers are segmented in a single image, then comprehensive cardiac analysis is enabled, but the mutual effects between chambers reduce the ability to distinguish and isolate each chamber
Solution Approach 1:
The patent applies different processing strategies to different regions: the ROI is processed with a first model considering all chambers collectively, while each subregion is processed with a second model focused on specific chambers. This local differentiation allows the system to maintain reliable comprehensive analysis while achieving precise individual chamber identification through region-specific modeling.
Solution Approach 2:
The patent introduces subregions as intermediary structures between the overall ROI and individual cardiac chambers. These subregions act as intermediate segmentation results that group related chambers together, allowing the system to transition from global to local processing. The subregions serve as mediators that reduce the direct mutual effects between all chambers by creating intermediate zones of focus.
Data Source
AI summary
A method for image segmentation may include acquiring an image including a region of interest (ROI). The ROI has a first margin, the ROI includes a subregion, and the subregion has a second margin. The method may further include acquiring a first model according to the ROI, wherein the first model has a third margin. The method may further determine, based on the first margin and the third margin, a second model by matching the first model with the image, wherein the second model includes a sub-model, and the sub-model has a fourth margin. The method may further include determining, based on the second margin, a third model by adjusting the fourth margin of the sub-model in the second model. The method may further include segmenting the ROI according to the third model and generating a segmented ROI based on a result of the segmentation.