Voxel-Based Calcification Analysis Using Correlation Weights
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Solution Overview
Problem
Current image processing methods for coronary artery calcification analysis in cardiovascular disease are inefficient, requiring significant manual correction and lacking full automation, which hampers accuracy and efficiency in identifying calcification regions from 3D CT images.
Innovation Solution
An image processing device and calcification analysis system that employs a voxel extractor and learner/predictor to generate and adjust vector weights based on correlation among voxels in 3D images, using convolutional neural networks and dilated convolutional neural networks to improve calcification index calculation, enabling fully automated and accurate analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If doctors manually analyze cardiac CT images to search for calcification areas, then analysis accuracy can be maintained, but significant manual time and labor are required
Solution Approach 1:
The system enables self-service automation where the image processing device automatically performs calcification detection and analysis without requiring doctor intervention for each case, while maintaining high accuracy through learned models
Solution Approach 2:
The patent replaces the mechanical manual analysis process with an automated image processing system using machine learning models that can detect calcification areas automatically, eliminating the need for manual image review while maintaining diagnostic accuracy
2Productivity
If semi-automatic image processing methods are used to search for calcification regions, then some manual work is reduced, but many errors still require doctor correction
Solution Approach 1:
The system incorporates feedback mechanisms where the model learns from correction data and continuously improves its accuracy, using the doctor's corrections as training feedback to reduce future errors and manual intervention needs
Solution Approach 2:
The patent adjusts model parameters and processing thresholds based on learned patterns from training data, optimizing the balance between automated detection accuracy and reduction of false positives that require manual correction
3Measurement precision
If traditional manual methods are used for calcification analysis, then analysis thoroughness can be maintained, but automation level remains low
Solution Approach 1:
The patent replaces manual analysis mechanics with automated machine learning-based image processing that can systematically evaluate all voxels in cardiac CT images, achieving both high automation and thorough detection through algorithmic analysis
Data Source
AI summary
The image processing device includes a voxel extractor, a learner, and a predictor. The voxel extractor extracts a target voxel and neighboring voxels adjacent to the target voxel from a 3D image. The learner generates vectors corresponding to the target voxel and the neighboring voxels, respectively, generates vector weights corresponding to each of the vectors, based on the vectors and a parameter group, and adjusts the parameter group, based on an analysis result of the target voxel generated by applying the vector weights to the vectors. The predictor generates vectors corresponding to the target voxel and the neighboring voxels, respectively, generates correlation weights among the vectors by applying a parameter group to the vectors, generates vector weights corresponding to each of the vectors by applying the correlation weights to the vectors, and generates an analysis result of the target voxel by applying the vector weights to the vectors.