Coronary Calcium Lesion Labeling via Branch Density Segmentation
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Solution Overview
Problem
Conventional methods for detecting coronary artery calcium (CAC) in medical images, such as CT scans, are inefficient due to the need for feature engineering in traditional machine learning and the inability of deep learning methods to accurately assign coronary artery branches to detected calcium lesions, leading to time-consuming processes and poor performance in segmentation.
Innovation Solution
A deep learning-based system that employs two encoder-decoder models: one for binary calcium segmentation and another for learning coronary artery branch density, allowing for the automatic detection and labeling of calcium lesions within specific arterial territories, eliminating the need for hand-crafted features and significantly improving processing efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional machine learning methods with feature engineering are used, then calcium deposits can be detected, but the process becomes time-consuming due to manual feature extraction
Solution Approach 1:
The patent replaces manual feature engineering (mechanical process) with automated deep learning feature extraction. The system uses neural networks to automatically learn relevant features from raw CT images, eliminating the time-consuming manual feature extraction process while maintaining detection accuracy.
Solution Approach 2:
The deep learning model performs self-service by automatically extracting features and detecting calcium deposits without requiring manual intervention. The system trains on labeled data and then autonomously processes new images, reducing dependency on expert annotation for each new case.
2Productivity
If deep learning methods are used for calcium detection, then processing efficiency improves, but the ability to assign coronary artery branches to lesions deteriorates
Solution Approach 1:
The patent segments the problem into two distinct tasks: (1) calcium deposit detection using deep learning for efficiency, and (2) coronary artery branch assignment using a separate algorithm. This segmentation allows each component to be optimized independently, maintaining high processing efficiency while recovering the lost branch assignment capability.
Solution Approach 2:
The system introduces an intermediary component that bridges the deep learning detector and the coronary artery labeling system. This intermediary processes the detected calcium locations and assigns them to specific coronary branches, preventing information loss while maintaining the efficiency gains from deep learning.
3Device complexity
If whole-heart calcium scoring is used, then processing is simplified, but vessel-specific calcium information is lost
Solution Approach 1:
The system segments the whole-heart calcium score into vessel-specific components by detecting calcium deposits and assigning them to individual coronary arteries. This provides both the simplicity of automated scoring and the detailed vessel-specific information needed for clinical decision-making.
Solution Approach 2:
The patent adds a new dimension of information by transitioning from a single whole-heart score to multiple vessel-specific scores. This dimensional expansion allows clinicians to see both the overall burden and the distribution across specific arteries, enabling more targeted treatment decisions.
Data Source
AI summary
Embodiments of the present disclosure include a method, device and computer readable medium involving receiving image data of one or more coronary arteries, generating a binary segmentation indicating presence of calcium in the one or more coronary arteries from the image data, generating a branch density of the one or more coronary arteries, and assigning a coronary artery label from the branch density to the binary segmentation such that at least one indication of presence of calcium of the binary segmentation is labeled as present in a specific one of the one or more coronary arteries.