Chest X-Ray Bone Density Estimation Using Bone Probability Heatmaps
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Solution Overview
Problem
Current methods for judging osteoporosis based on chest X-ray images lack accuracy and stability, as they do not effectively utilize the information from the entire image to provide reliable bone density estimates.
Innovation Solution
A method utilizing two convolutional neural networks (CNNs) to generate a bone probability heatmap and estimate bone density, where the first CNN generates a heatmap from an X-ray image and the second CNN uses this heatmap along with an enhanced image to improve estimation accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional methods are used to judge osteoporosis based on chest X-ray images, then the examination is easily accessible and common, but the accuracy and stability of bone density estimation are insufficient
Solution Approach 1:
The patent segments the bone density estimation task into two distinct stages: first generating a bone probability heatmap to identify bone regions, then using this heatmap as guidance for accurate density estimation. This segmentation allows the system to focus computational resources on relevant bone areas while maintaining overall image context, thereby improving both accuracy and stability of the estimation.
Solution Approach 2:
The bone probability heatmap serves as an intermediary between the raw X-ray image and the final bone density estimation. This intermediate representation highlights bone-specific features and suppresses interference from non-bone structures, enabling the second CNN to perform more reliable and accurate density estimation with improved stability.
2Loss of information
If the entire X-ray image is used for bone density detection, then comprehensive information is available, but interference from non-bone structures reduces estimation accuracy
Solution Approach 1:
The patent extracts bone-specific information from the entire X-ray image by generating a bone probability heatmap that isolates bone regions from non-bone structures. This extraction process removes interfering elements while preserving all relevant bone information, allowing the system to utilize comprehensive image data without the negative impact of non-bone interference.
Solution Approach 2:
The bone probability heatmap applies local quality enhancement by assigning different weights to different regions of the image - highlighting bone areas and suppressing non-bone areas. This local differentiation allows the system to maintain sensitivity to bone density variations while being insensitive to interference from surrounding structures.
Data Source
AI summary
A method and an electronic device are used to estimate osteoporosis. The method includes the following steps. An X-ray image is obtained. The X-ray image is input into a first convolutional neural network model to generate a bone probability heatmap of the X-ray image. A bone density estimation value is estimated based on the bone probability heatmap, the X-ray image, and a second convolutional neural network model.