AI Segmentation for Non-Contrast CT Vascular Structures
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Solution Overview
Problem
Current automated segmentation algorithms for non-contrast computed tomography (CT) imaging data are non-existent or inefficient, making it difficult to accurately assess vascular structures and detect pathological changes such as vessel stenoses or calcifications without the use of contrast agents.
Innovation Solution
A system comprising an input data interface, a segmentation module with an artificial intelligence model trained on segmented contrast CT imaging data, and an output data interface, which enables the automatic segmentation of non-contrast CT imaging data by leveraging existing algorithms developed for contrast CT imaging data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated segmentation algorithms are implemented for non-contrast CT imaging data, then productivity and accuracy of vascular assessment are improved, but the lack of contrast enhancement makes vessel differentiation more difficult (worsening measurement precision)
Solution Approach 1:
The patent introduces a contrast agent as an intermediary substance that enhances the visibility and differentiation of vascular structures in CT imaging data. The contrast agent accumulates in the blood vessels, creating sufficient contrast between vessels and surrounding tissues, thereby enabling automated segmentation algorithms to accurately detect and segment vascular structures without manual intervention.
2Measurement precision
If contrast agents are used to enhance vessel visibility, then measurement precision and vessel differentiation are improved, but patient safety is compromised due to allergic reactions and renal failure risks (worsening object-affected harmful factors)
Solution Approach 1:
The patent performs preliminary assessment of patient risk factors for contrast agent complications before administration. The system evaluates patient history, renal function, and allergy status to identify high-risk patients who should avoid contrast agents, thereby preventing harmful reactions before they occur.
Solution Approach 2:
The patent inverts the traditional approach by developing automated segmentation algorithms that can operate effectively on both contrast-enhanced and non-contrast CT imaging data. This dual-capability approach allows clinicians to choose non-contrast imaging for at-risk patients while maintaining automated segmentation functionality, thereby eliminating the need for contrast agents in vulnerable populations.
3Measurement precision
If manual annotation is used for training segmentation algorithms, then data accuracy is improved, but the acquisition of sufficient training data becomes extremely difficult and time-consuming (worsening loss of time and productivity)
Solution Approach 1:
The patent implements a self-service mechanism where the segmentation algorithm performs automated annotation of vascular structures in CT imaging data. The system uses the contrast-enhanced images to automatically identify, segment, and label vascular structures without requiring manual annotation by experts, thereby generating training data autonomously and eliminating the time-consuming manual annotation process.
Solution Approach 2:
The patent combines the segmentation and annotation functions into a single automated process. The segmentation algorithm simultaneously performs vessel segmentation and generates annotations for training data, merging multiple steps into one efficient operation that produces both the segmentation result and the training dataset.
4Reliability
If extensive high-quality image data with accurate annotations is collected for training, then model performance is improved, but data privacy and security regulations make data acquisition difficult (worsening ease of manufacture and data availability)
Solution Approach 1:
The patent extracts only the essential features and characteristics needed for training the segmentation algorithm, rather than requiring complete patient datasets. The system extracts vascular structure features, contrast enhancement patterns, and anatomical characteristics from CT images to create simplified training representations that maintain diagnostic performance while reducing privacy concerns.
Solution Approach 2:
The patent uses synthetic data generation and data augmentation techniques to create copies and variations of annotated training data. By generating synthetic contrast-enhanced CT images with realistic vascular structures and applying various transformations, the system expands the training dataset without acquiring additional patient data, thereby maintaining model performance while respecting data privacy regulations.
Data Source
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AI summary
The present invention relates to a system for providing an automatic segmentation for non-contrast computed tomography imaging data, the system comprising: an input data interface, configured to obtain imaging data comprising at least non-contrast computed tomography imaging data; a segmentation module, configured to implement a segmentation artificial intelligence model, which is adapted to generate a segmentation for the obtained non-contrast computed tomography imaging data, wherein the segmentation artificial intelligence model is trained at least with imaging data based on segmented contrast computed tomography imaging data; and an output data interface, configured to output the generated segmentation.