Ranked Vascular Path Selection From Images for Ambiguous Segmentation
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Solution Overview
Problem
Existing vascular segmentation methods struggle with low contrast and complex environments, leading to ambiguous results, and require significant human intervention, which is time-consuming and costly.
Innovation Solution
A semi-automatic method that combines manual and automatic techniques to segment vascular paths, using cost functions to rank and present path options, allowing users to select the most likely paths with minimal intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated analysis is used for vascular segmentation, then productivity is improved, but measurement precision deteriorates due to low contrast and complex environments
Solution Approach 1:
The patent introduces an intermediary cost function that bridges automated analysis and manual verification. The cost function processes image data and vascular paths to generate ranked path options, acting as a mediator that transforms complex image data into structured candidates for user selection, thereby maintaining both automation efficiency and precision.
Solution Approach 2:
The patent segments the vascular path identification process into distinct components: automated vascular path generation, cost function evaluation, and user selection. By dividing the workflow into these segments, the system achieves high productivity through automation while preserving measurement precision through structured user verification of ranked options.
2Measurement precision
If manual intervention is introduced to ensure quality results, then measurement precision is improved, but loss of time increases due to significant human effort required
Solution Approach 1:
The system performs preliminary automated analysis to generate multiple candidate vascular paths before user intervention. The cost function pre-evaluates these candidates and ranks them by likelihood, so that when users do intervene, they only need to review pre-processed and organized options rather than analyzing raw data from scratch, significantly reducing user time investment.
Solution Approach 2:
The cost function automatically evaluates and ranks vascular paths based on image data and anatomical constraints, performing self-service analysis that reduces the burden on users. The system serves itself by generating structured path options with associated confidence scores, requiring minimal user effort to achieve high measurement precision.
3Measurement precision
If multiple path options are generated and presented to users, then measurement precision is improved through selection, but device complexity increases due to cost function and ranking system
Solution Approach 1:
The cost function evaluates vascular paths by changing and comparing multiple parameters including image intensity continuity, vascular width consistency, and anatomical constraint satisfaction. By systematically varying and weighting these parameters, the system generates ranked path options that improve measurement precision while managing complexity through parameter-based evaluation rather than complex structural additions.
4Productivity
If automated methods are used in complex environments with low contrast, then productivity is improved, but reliability deteriorates due to ambiguous results
Solution Approach 1:
The cost function provides feedback by evaluating each generated vascular path against image data and anatomical constraints, assigning scores that indicate reliability. This feedback mechanism allows the system to maintain high productivity through automated processing while improving reliability by presenting users with confidence-ranked options that reflect the reliability of each segmentation result.
Data Source
AI summary
Methods and systems for manually assisted definition of vascular features are described. In some embodiments, a method provides for editing of vascular paths by enabling a user to drag an erroneously segmented region of a selected vascular path into alignment with a more correctly segmented position that is depicted as a blood vessel in a vascular image. The method may use an energy function, defined as a function of position along the segmentation of the selected blood vessel, to determine how a vascular path is to be moved based on dragging motions provided by the user. In some instances, non-zero regions of the energy function are set based on the position of the selected region.