Abdominal CT Muscle and Fat Quantification via Compartment Segmentation
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Solution Overview
Problem
Conventional methods for assessing body composition using CT scans are inefficient, particularly in quantifying abdominal muscle due to variability in muscle shape and overlap with other tissues, limiting their practicality for large-scale clinical or research applications.
Innovation Solution
A semi-automated system and method for segmenting and quantifying abdominal muscle and fat from CT images, using threshold methods to identify and calculate the areas of subcutaneous fat, muscle, and visceral fat compartments, allowing for efficient and accurate analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual segmentation of muscle area is performed, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The abdominal cross-section is divided into three distinct compartments: subcutaneous fat compartment, muscle compartment, and visceral fat compartment. This spatial segmentation allows automated threshold-based quantification of muscle area within the middle compartment, resolving the contradiction by enabling rapid automated measurement while maintaining precision through anatomically-based compartmentalization.
Solution Approach 2:
The patent introduces an intermediary threshold-based automated segmentation method that mediates between manual segmentation (high precision) and fully automated methods (high speed). The threshold method acts as an intermediary approach that achieves both speed and acceptable precision by leveraging the known HU ranges of different tissue types.
2Productivity
If automated fat segmentation using threshold methods is applied, then productivity is improved, but measurement precision deteriorates
Solution Approach 1:
By segmenting the abdominal cross-section into three anatomically-defined compartments, the patent enables automated threshold-based quantification to achieve both speed and precision. The muscle compartment is clearly defined as the middle compartment between subcutaneous and visceral fat, allowing accurate automated measurement without the ambiguity that plagues single-compartment approaches.
Solution Approach 2:
The patent applies different threshold criteria and compartment definitions to different regions of the abdominal cross-section. The muscle compartment is specifically identified as the middle compartment with distinct HU characteristics, allowing localized optimized quantification that maintains precision while enabling automated processing.
3Measurement precision
If CT imaging is used to assess body composition, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent simplifies the image processing complexity by segmenting the abdominal cross-section into three distinct compartments with clear threshold definitions. This segmentation approach transforms the complex problem of automated muscle quantification into a simpler threshold-based classification problem, maintaining CT's high measurement precision while reducing processing complexity.
Solution Approach 2:
The patent utilizes changes in Hounsfield unit parameters to distinguish between different tissue types. By defining specific HU ranges for subcutaneous fat, muscle, and visceral fat, the patent converts the complex image processing problem into a straightforward parameter-based classification, maintaining precision while simplifying the processing algorithm.
Data Source
AI summary
A system and method for quantifying muscle and fat from abdominal image data. An input is configured to receive the abdominal image data from a CT imaging system. A non-transitive computer-readable storage medium having stored thereon instructions. A processor is configured to receive the abdominal image data and access the storage medium to execute instructions. The executed instructions perform automated segmentation of the abdominal image data into at least one of a subcutaneous fat compartment, a muscle compartment, or a visceral fat compartment. One or more of the compartments are compared to a predetermined threshold to identify corresponding boundaries thereof. An area of one or more of the compartments is calculated based on the corresponding boundaries and predetermined threshold. A display is coupled to the processor and configured to display the at least one subcutaneous fat compartment, muscle compartment, or visceral fat compartment on the abdominal image data.