CT-Based Lung Disease Grading Through Affected-Volume Analysis
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Solution Overview
Problem
Current medical imaging technologies can only provide positive diagnoses for lung diseases but fail to determine the severity of these diseases, which is crucial for timely treatment planning, especially for rapidly spreading conditions like novel coronavirus pneumonia.
Innovation Solution
A diagnostic information processing method that involves acquiring lung medical images, extracting image parameters, particularly the volume of affected areas, and using a neuron network to determine disease grades by comparing volumes with a relationship table or calculating volume proportions, enabling disease grading and tracking disease development.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If CT imaging is used to detect lung diseases, then disease detection capability is improved, but disease severity assessment capability deteriorates
Solution Approach 1:
The patent extracts quantitative features from CT images, including volume of affected areas, CT value distribution intervals, and other measurable parameters. These extracted features are then used to assess disease severity, transforming qualitative imaging data into quantitative severity metrics that enable grading and monitoring.
Solution Approach 2:
The patent changes the parameter representation from binary detection (present/absent) to quantitative continuous parameters such as volume measurements, CT value distributions, and temporal changes. This parameter transformation enables the system to differentiate between disease grades and track progression, resolving the information loss problem.
2Measurement precision
If manual disease grading is performed, then accuracy is improved, but processing time and efficiency deteriorate
Solution Approach 1:
The patent implements an automated grading system that performs disease severity assessment without requiring manual intervention. The system automatically extracts features, calculates volumes, determines CT value distributions, and assigns disease grades based on pre-established criteria, enabling rapid processing while maintaining accuracy through algorithmic consistency.
Solution Approach 2:
The patent replaces manual radiological assessment with computational algorithms and image processing systems. The mechanical process of manual measurement and evaluation is substituted with automated digital processing, including computer-based volume calculation, feature extraction, and decision support systems that provide rapid grading.
3Loss of information
If comprehensive disease grading is implemented, then diagnostic information completeness is improved, but system complexity deteriorates
Solution Approach 1:
The patent segments the complex diagnostic task into distinct modular components: image acquisition, feature extraction, volume calculation, CT value analysis, and grading decision. Each module handles a specific aspect of the grading process independently, making the overall system more manageable and easier to implement while providing comprehensive diagnostic information through the integration of these segments.
Data Source
AI summary
Disclosed are a diagnostic information processing method and apparatus based on a medical image, and a storage medium, to achieve disease grading based on a medical image. The method includes: acquiring a first lung medical image of a subject; acquiring image parameters of an affected area in the first lung medical image; and determining, according to the image parameters of the affected area, a disease grade of lungs of the subject corresponding to information of the first lung medical image. Using the solution provided by the present invention, the image parameters of the affected area in the first lung medical image can be acquired, and then the disease grade of the lungs of the subject corresponding to the information of the first lung medical image can be determined according to the image parameters of the affected area, so that a disease can be graded based on a medical image.