CNN Coronary Artery Segmentation via Jerman Filter Preprocessing
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Solution Overview
Problem
Current image processing systems are unable to efficiently perform autonomous segmentation of contrast-filled coronary artery vessels in x-ray images, which is crucial for accurate diagnosis and monitoring of coronary artery diseases.
Innovation Solution
A computer-implemented method using a convolutional neural network (CNN) with preprocessing steps like the Jerman filter and data augmentation, trained on x-ray angiography scans with binary masks to extract and segment coronary vessels, improving segmentation accuracy through residual connections and optimized training procedures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional image processing systems are used for coronary artery vessel segmentation, then the system complexity remains low, but the segmentation accuracy and reliability are insufficient
Solution Approach 1:
The patent replaces traditional mechanical image processing methods with a neural network-based system. The neural network automatically learns features and performs segmentation, substituting manual feature extraction and traditional algorithms with an intelligent system that achieves superior accuracy while handling the complexity internally.
Solution Approach 2:
The patent transforms the segmentation problem by changing the approach from fixed thresholding and manual parameter tuning to adaptive parameter learning. The neural network automatically adjusts segmentation parameters based on learned patterns from training data, improving accuracy without requiring explicit parameter specification by users.
2Productivity
If manual segmentation methods are used, then the system remains simple, but the productivity and time efficiency are low
Solution Approach 1:
The patent implements preliminary action through an offline training phase where the neural network learns from annotated training data. This pre-learning process enables the system to perform rapid automated segmentation during actual use, significantly improving productivity while reducing the time required for each segmentation task compared to manual methods.
3Reliability
If traditional segmentation algorithms are applied, then false positives occur frequently, but using complex neural networks may increase computational resource consumption
Solution Approach 1:
The patent applies segmentation by dividing the coronary artery segmentation task into distinct neural network components: a U-Net architecture for vessel segmentation and a separate refinement network for boundary optimization. This modular approach improves reliability by addressing different aspects of segmentation separately while managing computational resources efficiently through specialized processing for each component.
Data Source
AI summary
A computer-implemented method for autonomous segmentation of contrast-filled coronary artery vessels, the method comprising the following steps: receiving (101) a x-ray angiography scan representing a maximum intensity projection of a region of anatomy that includes the coronary vessels on the imaging plane; preprocessing (102) the scan to output a preprocessed scan; and performing autonomous coronary vessel segmentation (103) by means of a trained convolutional neural network (CNN) that is trained to process the preprocessed scan data to output a mask denoting the coronary vessels.