Automated Flow Diverter Detection in Medical Imaging
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Manual adjustment of flow diverters in medical imaging is time-consuming and difficult due to their small size and similarity to surrounding tissues, leading to increased costs and potential inaccuracies in clinical evaluation.
Innovation Solution
An automated method for detecting flow diverters using medical imaging data, which calculates centerline and cross-section scores to identify and highlight the flow diverter, employing machine-learning algorithms and shape models to enhance visualization and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual adjustment of visualization is performed to locate the flow diverter, then the flow diverter can be made visible for clinical evaluation, but the process becomes difficult and time consuming
Solution Approach 1:
The system automatically performs the visualization adjustment without requiring manual intervention. The processor autonomously identifies the flow diverter location and generates optimized visualization images, allowing the system to serve itself rather than requiring clinician manual adjustment.
Solution Approach 2:
The manual mechanical process of visual adjustment is replaced with an automated computational system. The processor uses algorithms to automatically locate and highlight the flow diverter, substituting the manual mechanical adjustment with digital image processing and automated detection.
2Measurement precision
If manual adjustment of visualization is performed to locate the flow diverter, then the flow diverter can be made visible, but the process becomes complex and time consuming
Solution Approach 1:
The system automatically performs the detection and visualization without requiring complex manual operations. The processor autonomously executes the entire detection pipeline, eliminating the need for clinicians to manually adjust multiple parameters and reducing operational complexity.
Solution Approach 2:
The complex detection process is segmented into distinct automated steps: centerline identification, cross-section analysis, score calculation, and image generation. This segmentation allows each component to be optimized independently while the entire process remains automated, reducing overall system complexity.
3Productivity
If automated detection method is implemented, then the time and effort for flow diverter visualization is reduced, but the system complexity increases
Solution Approach 1:
The automated detection system is segmented into modular components: centerline detection module, cross-section analysis module, scoring module, and image generation module. This segmentation allows each module to be developed and optimized independently, making the overall system more manageable despite the increased automation.
Solution Approach 2:
The system introduces an intermediary processing layer between the raw imaging data and the final visualization. The processor acts as an intermediary that automatically extracts features, calculates scores, and generates optimized images, bridging the gap between raw data and clinical evaluation without requiring manual intervention.
Data Source
AI summary
A flow diverter is automatically detected from medical imaging data. The appearance of the flow diverter as represented in the data as well as the geometry or shape of the flow diverter is used in the detection. Using scoring for appearance relative to the centerline and cross-section of the centerline, the flow diverter is detected for increasing visualization.