Anatomical Model Heart Segmentation
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Solution Overview
Problem
Current medical imaging systems face challenges in heart segmentation due to variability in image intensity caused by contrast agents and patient metabolism, making intensity-based segmentation tools unreliable, especially with protocols like saline flush.
Innovation Solution
A novel approach using a model of the anatomical feature for automatic segmentation, where a global alignment is performed to match the model with the patient's image data, followed by local alignment of internal structures, allowing for deformation to achieve accurate segmentation without relying on intensity consistency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If intensity-based segmentation tools are used, then segmentation can be performed based on image intensity, but reliability deteriorates due to variability in image intensity caused by contrast agents and patient metabolism
Solution Approach 1:
The patent uses a pre-established anatomical model (copy) of the heart and its chambers to replace direct intensity-based segmentation. The model is aligned with the patient's image data through global and local alignment algorithms, allowing segmentation to be performed by matching the model's structural features rather than relying on intensity variations. This copying approach maintains segmentation reliability despite intensity variability caused by contrast agents and metabolism.
Solution Approach 2:
The patent performs preliminary actions by pre-establishing an anatomical model with known structural features before segmentation. The model includes predefined chambers and their spatial relationships, which are then aligned with the patient's image data. This preliminary preparation allows the segmentation to proceed without relying on intensity-based decisions during the actual segmentation process, thereby improving reliability.
2Reliability
If model-based segmentation with global and local alignment is performed, then segmentation reliability is improved, but device complexity increases
Solution Approach 1:
The patent divides the alignment and segmentation process into distinct segments: global alignment (matching overall heart position and orientation) and local alignment (matching individual chamber positions and shapes). This segmentation of the processing steps manages complexity by breaking down the complex task into manageable modules, each with a specific function, while maintaining high reliability through the combination of both alignment stages.
Solution Approach 2:
The patent introduces an anatomical model as an intermediary between the raw image data and the final segmentation result. The model serves as a mediator that bridges the gap between variable intensity data and stable anatomical structures. By using this intermediary, the system achieves reliable segmentation without directly comparing intensity values, effectively managing the complexity through an intermediate representation.
3Device complexity
If intensity-based segmentation is used, then the process is simple, but accuracy deteriorates due to variations in contrast agent injection and patient metabolism
Solution Approach 1:
The patent replaces intensity-based measurement with model-based copying. Instead of measuring intensity values to identify chambers, the system copies the known anatomical structure from the pre-established model and aligns it with the image data. This approach maintains simplicity by using a straightforward alignment process while dramatically improving accuracy by eliminating dependence on variable intensity measurements affected by contrast agents and metabolism.
Data Source
AI summary
A technique is provided for utilizing a model of an anatomical feature to facilitate the segmentation of the anatomical feature from its background in a medical image. A global alignment of the model with a region in the patient's image data that generally corresponds to the anatomical feature is performed in one embodiment. Internal structural features within the model are then aligned with their corresponding structural features in the patient's image data. The portion of the patient's image data that is aligned with the model of the anatomical feature is then segmented from the remaining portions of the patient's image data that are not aligned with the model.