2D Vessel Segmentation Orientation Detection for Reliable Angio-FFR
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Inaccurate fluid dynamics assessments in vessel segmentation due to incorrect selection of starting and ending positions during vessel segmentation, leading to inverted blood flow direction and incorrect modeling results.
Innovation Solution
An apparatus and method for automatically detecting and verifying the vessel segmentation orientation using a trained classifier to determine the correct orientation of vessel segmentation, providing an indication to the user and potentially correcting the segmentation order.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual selection of starting and ending positions is used for vessel segmentation, then user control and flexibility are improved, but segmentation orientation errors increase leading to incorrect fluid dynamics assessments
Solution Approach 1:
The system performs fluid dynamics simulation in both proximal-to-distal and distal-to-proximal directions, then compares the results to determine which orientation produces physiologically plausible values. This feedback mechanism automatically detects and corrects segmentation orientation errors without requiring user intervention, thus maintaining ease of operation while significantly improving reliability.
Solution Approach 2:
The system performs preliminary fluid dynamics simulations in both possible orientations before finalizing the assessment. By pre-calculating both proximal-to-distal and distal-to-proximal simulations and comparing results, the system proactively identifies orientation errors before they affect the final clinical decision, ensuring accurate assessments without adding significant user burden.
2Measurement precision
If automated classifier is used to detect vessel segmentation orientation, then segmentation orientation accuracy is improved, but device complexity increases
Solution Approach 1:
The system replaces complex manual orientation verification with an automated computational approach. Instead of requiring users to manually verify orientation or implement complex image analysis algorithms, the system uses fluid dynamics simulation as a virtual sensor to automatically detect orientation errors through physiological plausibility checks, substituting mechanical/visual verification with computational analysis.
Solution Approach 2:
The system performs self-verification of segmentation orientation by automatically running simulations in both directions and comparing results. The fluid dynamics model serves as its own validation mechanism, eliminating the need for separate complex orientation detection algorithms or manual user verification, thus improving accuracy while keeping the system relatively simple.
3Reliability
If fluid dynamics simulation is performed in both directions to verify orientation, then assessment reliability is improved, but computational time and resources increase
Solution Approach 1:
The system performs fluid dynamics simulations in both proximal-to-distal and distal-to-proximal directions, which is excessive action since only one orientation is ultimately correct. However, this redundancy is necessary to detect orientation errors. The system then uses the comparison results to identify the correct orientation, accepting the additional computational burden as a trade-off for ensuring assessment reliability in clinical applications.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
An apparatus for assessing a vessel of interest and a corresponding method are provided in which the modeling of the hemodynamic parameters using a fluid dynamics model can be verified by deriving feature values from the segmented vessel of interest and inputting these feature values into a classifier. The classifier may then determine, based on the feature values whether the segmentation has been performed from proximal to distal, from distal to proximal or cannot be determined from the provided data. An incorrect segmentation order can thus be identified and potentially be corrected, thereby avoiding inaccurate simulation results.