Anatomical Tree Model Generation via Hierarchical Segment Connection
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Solution Overview
Problem
Current minimally invasive medical techniques face challenges in accurately generating anatomical tree structures and performing image segmentation for navigating medical instruments within patient anatomy, leading to potential misclassification of anatomical features and insufficient accuracy for clinical applications.
Innovation Solution
A method and system for building anatomical branch models by receiving anatomical image data, determining parent and child segments, calculating connection costs, and generating an image of the anatomical branch model based on relationships between these segments, which includes identifying and connecting child segments to parent segments to form accurate segment sections.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional image segmentation methods are used to generate anatomical tree structures, then the process is simpler and faster, but the accuracy and reliability of the generated models are insufficient
Solution Approach 1:
The patent applies segmentation by dividing the anatomical image data into discrete graphical units and further segmenting them into parent segments and child segments. This hierarchical segmentation allows for more precise control over the anatomical tree structure generation, improving accuracy while managing complexity through systematic breakdown of the segmentation process.
Solution Approach 2:
The patent utilizes parameter changes by calculating connection costs based on multiple parameters including intensity values, gradient orientations, and geometric relationships between graphical units. By dynamically adjusting and evaluating these parameters, the system achieves higher accuracy in determining anatomical structures while providing a structured approach to manage the complexity of parameter evaluation.
2Manufacturing precision
If detailed image segmentation is performed to improve anatomical model accuracy, then the precision increases, but the computational time and processing complexity increase
Solution Approach 1:
The patent applies preliminary action by pre-calculating and storing intensity values, gradient orientations, and other geometric parameters for each graphical unit before performing the actual segmentation and tree structure generation. This preprocessing step allows for faster execution of the main segmentation algorithm while maintaining high precision, as the computationally intensive parameter calculations are performed in advance.
Solution Approach 2:
The patent implements partial action by selectively applying detailed segmentation and connection cost calculation only to relevant graphical units that contribute to anatomical tree structures, rather than processing all image data with equal detail. This approach maintains high precision for critical anatomical features while reducing overall computational time by avoiding excessive processing of non-relevant areas.
3Reliability
If connection costs are calculated for all possible parent-child segment combinations, then the accuracy of segment relationships improves, but the computational complexity increases
Solution Approach 1:
The patent applies segmentation by organizing graphical units into hierarchical parent segments and child segments, which divides the connection cost calculation into manageable subsets. Instead of evaluating all possible combinations across the entire image, the segmentation structure allows for localized evaluation within each parent-child relationship, improving reliability while reducing computational complexity through structured organization.
Solution Approach 2:
The patent implements local quality by calculating connection costs with high precision for specific parent-child segment relationships that are locally relevant to anatomical structures. The method evaluates intensity values, gradient orientations, and geometric relationships locally at each potential connection point, ensuring high accuracy for critical relationships while avoiding unnecessary complex calculations for less significant connections.
Data Source
AI summary
A method of building an anatomical branch model comprises receiving anatomical image data comprising a plurality of graphical units associated with an anatomical structure and determining a plurality of parent segments and child segments. The method also comprises determining a set of relationships between the parent segments and the child segments by determining a first set of connection costs of connecting at least one of the parent segments to a first subset of the child segments, the child segments of the first subset are separated from the at least one of the parent segments by one or more gaps, identifying a first child segment from the first subset of the child segments based on a first connection cost, and connecting the first child segment to the at least one parent segment. The method further comprising generating an image of the anatomical branch model based on the determined set of relationships.