Anatomical Structure Labeling With Rule-Based Tree Segmentation
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Solution Overview
Problem
Existing techniques struggle to accurately determine label sets for complex anatomical structures in medical imaging datasets, particularly for structures like the coronary artery, which consist of multiple substructures arranged in sequences and bifurcations.
Innovation Solution
A method and system for determining a label set using a tree-structured segmentation of medical imaging data, incorporating a ruleset of anatomical interrelationships and candidate labels with associated probabilities, and a recursive selection algorithm to optimize label assignment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing techniques are used to determine label sets for complex anatomical structures, then the process is simpler, but the accuracy of labeling is insufficient
Solution Approach 1:
The patent segments the labeling problem into multiple components: tree-structured segmentation of the anatomical structure, candidate label generation for each section, probability assignment, and rule-based validation. This segmentation allows the system to handle complex anatomical structures by breaking them down into manageable sections while maintaining overall accuracy.
Solution Approach 2:
The patent introduces an intermediary rule set that mediates between the probabilistic candidate labels and the final label assignment. This rule set encodes anatomical knowledge and constraints, acting as a mediator that ensures the selected labels are not only probabilistically likely but also anatomically valid, thereby improving labeling accuracy without requiring direct complex modeling.
2Adaptability or versatility
If a simple labeling approach is used, then the system is easier to operate, but it cannot handle complex anatomical structures with multiple substructures and bifurcations
Solution Approach 1:
The patent creates a universal labeling framework that can handle various types of anatomical structures (arteries, vessels, bronchi, etc.) with different complexities. The tree-structured segmentation and rule-based approach provide a multi-functional system that adapts to different anatomical structures without requiring structure-specific customization, thereby improving versatility while maintaining operational simplicity.
Solution Approach 2:
The patent performs preliminary actions by pre-defining the tree structure of the anatomical structure and pre-generating candidate labels with probabilities for each section. This preliminary preparation allows the system to handle complex structures efficiently during the actual labeling process, reducing operational complexity while maintaining high adaptability.
3Loss of information
If probabilistic candidate labels are generated for each section, then more information is available, but the selection process becomes more complex
Solution Approach 1:
The patent implements feedback through the rule-based validation process. The rule set provides feedback on whether selected labels are anatomically valid, allowing the system to iteratively refine label selections. This feedback mechanism ensures that information from probabilistic candidates is retained and utilized while guiding the selection process toward anatomically correct solutions without requiring complex optimization algorithms.
Solution Approach 2:
The patent changes the parameter representation by converting continuous probabilistic scores into discrete label selections constrained by anatomical rules. This parameter transformation simplifies the selection process by moving from continuous probability optimization to discrete rule-based validation, retaining information while reducing computational complexity.
Data Source
AI summary
Various examples of the disclosure pertain to determining a label set for an anatomical structure such as a complex blood vessel, e.g., the coronary artery. The determining of the label set takes into account multiple inputs, such as the rule set of anatomical relationship between sub structures of the anatomical structure and a list of candidate labels and associated probabilities obtained for each one of the anatomical substructures.