Automatic Tissue Segmentation via Local Voxel Neighborhood Analysis
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Solution Overview
Problem
Current methods for segmenting and quantifying medical images, particularly for differentiating between visceral and subcutaneous adipose tissue, face challenges such as inter and intra-observer variability, reliance on specific anatomical characteristics, and failure in cases with discontinuities or thin tissue layers, leading to inaccurate tissue differentiation and quantification.
Innovation Solution
A computer-implemented method that automatically differentiates between tissues by establishing parameters, defining a differentiation region, and performing local evaluation within that region, using techniques like histogram analysis, ray tracing, and active contours to reduce misclassification and handle complex anatomical scenarios.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual or direct methods are used for tissue segmentation, then expert judgment is applied, but inter and intra-observer variability reduces reliability
Solution Approach 1:
The system performs automatic tissue segmentation and quantification without requiring expert intervention. The computational algorithm independently identifies and differentiates tissue types based on image data, eliminating observer variability while maintaining diagnostic reliability through automated decision-making processes
Solution Approach 2:
The patent replaces manual expert assessment with an automated computational system that uses image processing algorithms to segment and quantify tissues. This substitution eliminates human variability in measurement while maintaining or improving precision through consistent application of segmentation criteria
2Ease of operation
If interactive methods are used to facilitate contour tracing, then expert work is assisted, but significant effort is still required which can skew judgment
Solution Approach 1:
The system automatically performs contour identification and tissue segmentation without requiring expert users to manually trace contours. The algorithm independently processes the image data to define tissue boundaries, eliminating the time-consuming manual tracing process while providing assistance to experts through automated results
3Extent of automation
If semi-automatic methods using global segmentation techniques are used, then tissue differentiation is automated, but active user intervention is still necessary due to anatomical peculiarities
Solution Approach 1:
The system applies local evaluation to each voxel by analyzing its neighborhood characteristics rather than using global segmentation techniques. This local approach adapts to anatomical variations and peculiarities in each specific region, eliminating the need for user intervention to correct for anatomical diversity while maintaining high automation
Solution Approach 2:
The patent divides the image into individual voxels and evaluates each voxel's neighborhood separately to determine tissue type. This fine-grained segmentation approach allows the system to handle anatomical variations at the local level without requiring global adjustments or user intervention
4Device complexity
If methods relying on specific anatomical characteristics are used, then tissue differentiation is simplified, but accuracy fails in cases with discontinuities or thin tissue layers
Solution Approach 1:
The system evaluates each voxel based on its local neighborhood characteristics rather than relying on global anatomical assumptions. This local approach allows the system to accurately identify tissues even when anatomical patterns are disrupted by discontinuities or thin layers, maintaining precision without increasing overall method complexity
Solution Approach 2:
The patent changes the evaluation parameter from global anatomical patterns to local voxel neighborhood characteristics. By analyzing the immediate surroundings of each voxel, the system can detect tissue types based on local intensity patterns rather than relying on expected anatomical structures, thereby maintaining accuracy in cases with discontinuities
Data Source
AI summary
A computer-based method is herein disclosed, allowing to differentiate automatically between two tissues of interest: an extrinsic and an intrinsic tissue, from a plurality of images, obtaining a quantitative assessment of each of said tissues without requiring the intervention of an expert. Said method involves the definition of a differentiation region in images obtained from a medical imaging acquisition device using a parametric contour, after which differentiation and quantification are carried out based on the photometric characteristics of the different tissues observed in images, evaluating the local neighborhood of each voxel belonging to the differentiation region previously defined in the plurality of images. The disclosed method increases to a great extent precision in differentiation and quantification of tissues, while the shown percentage error is considered tolerable for diagnostic purposes.