Liver Disease Detection via CT Image Shape and Texture Analysis
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Solution Overview
Problem
Current methods for diagnosing liver disease, particularly cirrhosis, are invasive and carry risks such as pain, bleeding, and mortality, and suffer from sampling errors and intra-observer discrepancies, prompting the need for noninvasive detection techniques.
Innovation Solution
A computer-implemented method using image data from imaging techniques like CT scans to extract liver metrics and apply statistical models predictive of liver conditions, such as cirrhosis, by analyzing shape and texture changes, which can be implemented in a system comprising an imaging device and a computing device with image processing and data analysis capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a liver biopsy is performed to detect cirrhosis, then diagnostic accuracy is improved, but patient safety deteriorates due to risks of pain, bleeding, and mortality
Solution Approach 1:
The patent creates a virtual copy of the liver from CT scan images and analyzes this digital replica to detect cirrhosis. The system extracts shape features (volume, surface area, curvature) and texture features from the virtual liver model, eliminating the need for physical biopsy while maintaining diagnostic accuracy through quantitative image analysis
Solution Approach 2:
The patent replaces the mechanical biopsy procedure with a computational imaging system. Instead of physically extracting liver tissue, the system uses computer algorithms to analyze CT scan images and automatically detect cirrhosis based on shape and texture characteristics, substituting mechanical intervention with digital analysis
2Measurement precision
If a liver biopsy is performed to detect cirrhosis, then diagnostic accuracy is improved, but reliability deteriorates due to sampling error and geographic distribution variability
Solution Approach 1:
The patent segments the liver into multiple regions of interest based on CT scan images and analyzes shape and texture features across different liver segments. This allows comprehensive assessment of the entire liver volume rather than sampling a single localized area, improving reliability by capturing the geographic distribution of disease throughout the organ
3Measurement precision
If pathologists manually assess liver biopsy samples, then diagnostic capability is improved, but consistency deteriorates due to intra-observer discrepancy
Solution Approach 1:
The patent implements a self-service diagnostic system where the CT scan images and automated algorithms perform the analysis independently of human pathologists. The system automatically extracts shape features (volume, surface area, curvature) and texture features from the liver, providing consistent, reproducible results without the variability inherent in manual pathological assessment
Data Source
AI summary
A noninvasive, quantitative imaging technique is presented for detecting and diagnosing liver disease, such as cirrhosis. The technique includes: capturing scan data from a subject using computed tomography or another type of imaging method and extracting image data representing the liver from the scan data. Various measures of the liver may be obtained from image data and then used to compute random variables of a statistical model, where the model is predictive of a medical condition of the liver and comprised of random variables that are indicative of at least one of a shape or texture of the liver. Output from the statistical model provides an indication of an undesirable condition of the liver.