Automatic Anatomy Recognition for Body Composition Quantification
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Solution Overview
Problem
Current methods for body composition analysis, such as anthropometry, bioelectrical impedance analysis, and radiologic imaging, face challenges in accurately quantifying subcutaneous adipose tissue, visceral adipose tissue, muscle tissue, and bone tissue due to limitations in precision, radiation exposure, and labor-intensiveness, particularly in whole-body or large-scale assessments.
Innovation Solution
The development of Automatic Anatomy Recognition (AAR) methods, including AAR-BCA and AAR-DQ, which utilize fuzzy anatomy models and virtual landmarks to automate the localization and quantification of tissue components and disease burden in CT, PET/CT, and PET/MR images, decoupling the need for explicit segmentation and correcting for partial volume effects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual segmentation methods are used for body composition analysis, then measurement precision can be maintained, but productivity is significantly reduced due to labor-intensiveness
Solution Approach 1:
The system performs automatic anatomy recognition and tissue quantification without requiring manual segmentation by clinicians. The AAR algorithm independently identifies anatomical structures, segments tissue components, and quantifies body composition metrics, enabling the system to serve itself rather than relying on manual intervention while maintaining diagnostic accuracy
Solution Approach 2:
The patent replaces manual mechanical segmentation operations with an automated computational imaging system. The AAR methodology uses algorithm-based anatomical recognition and fuzzy logic to automatically delineate tissue boundaries and quantify composition, substituting the mechanical manual tracing process with an automated image processing pipeline that maintains precision while dramatically improving throughput
2Measurement precision
If DXA is used for body composition quantification, then measurement precision is high, but object-affected harmful factors increase due to radiation exposure
Solution Approach 1:
The system is designed to work with multiple imaging modalities including CT, PET/CT, and PET/MR images. By leveraging the anatomical detail already captured by these diagnostic imaging systems, the AAR methodology extracts body composition information without requiring additional radiation-exposing scans, thus serving multiple functions with a single imaging acquisition
Solution Approach 2:
The patent analyzes existing imaging data by changing the parameters of analysis rather than acquiring new radiation-exposing images. The AAR system processes CT, PET/CT, or PET/MR images using different segmentation and quantification parameters to derive body composition metrics, transforming existing diagnostic images into compositional data without additional radiation exposure
3Productivity
If automatic anatomy recognition is implemented, then productivity is improved through automation, but device complexity increases
Solution Approach 1:
The AAR methodology segments the complex task of body composition analysis into distinct processing stages: anatomical structure identification, tissue boundary delineation, and quantification calculation. This segmentation of the algorithm into modular components manages complexity by breaking down the overall process while maintaining high automation and throughput
Solution Approach 2:
The patent introduces virtual landmarks as intermediary elements that facilitate automatic anatomical recognition. These virtual landmarks serve as reference points that guide the segmentation process and simplify the complexity of directly identifying complex tissue boundaries, acting as mediators between the imaging data and the quantification algorithms
4Ease of operation
If threshold-based methods are used for tissue quantification, then ease of operation is improved, but measurement precision deteriorates due to inability to distinguish different compartments
Solution Approach 1:
The AAR methodology applies different segmentation strategies and intensity thresholding parameters to different anatomical regions and tissue types. Rather than using a single global threshold, the system adapts local segmentation parameters to the specific characteristics of each tissue compartment, maintaining ease of automated operation while achieving precise differentiation between subcutaneous and visceral adipose tissue, muscle, and bone
Data Source
AI summary
Quantification of body composition plays an important role in many clinical and research applications. Radiologic imaging techniques such as Dual-energy X-ray absorptiometry, magnetic resonance imaging (MRI), and computed tomography (CT) imaging make accurate quantification of the body composition possible. This disclosure presents an automated, efficient, accurate, and practical body composition quantification method for low dose CT images; method for quantification of disease from images; and methods for implementing virtual landmarks.