Canonical Ultrasound View Extraction Using Reference Image Matching
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Solution Overview
Problem
Existing ultrasound imaging systems face challenges in automating the acquisition of standardized views, which are crucial for accurate clinical interpretation, due to integration difficulties with AI-driven automation and regulatory hurdles in diverse healthcare settings.
Innovation Solution
An apparatus and method that utilize a processor and memory to receive ultrasound data, classify canonical views using an extractor, generate comparisons with reference views, and extract matched canonical views based on a similarity metric, displayed through a graphical user interface.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If AI-driven automation is integrated into ultrasound imaging systems, then automated acquisition of standardized views is improved, but device complexity and regulatory approval difficulties increase
Solution Approach 1:
The system segments the automated view acquisition process into distinct functional modules: a view classification module that identifies canonical views from ultrasound data, a comparison module that matches identified views against reference views, and an extraction module that outputs matched views. This modular segmentation reduces integration complexity by allowing each module to be developed and validated independently while maintaining automated functionality.
Solution Approach 2:
The system introduces reference views as an intermediary standard between the AI-driven view identification process and the final automated acquisition output. These reference views serve as a mediating layer that enables regulatory validation by providing a known, traceable standard against which automated performance can be measured and verified, thus reducing regulatory approval difficulties.
2Extent of automation
If AI-driven automation is integrated into ultrasound imaging systems, then automated acquisition of standardized views is improved, but regulatory approval difficulties increase
Solution Approach 1:
The system implements feedback mechanisms where identified canonical views are continuously compared against reference views, and the results are used to validate and refine the automated acquisition process. This feedback loop provides traceable validation data that demonstrates consistent performance against known standards, thereby improving regulatory approval reliability by showing measurable, repeatable results.
Solution Approach 2:
The system performs preliminary validation by comparing identified views against reference views before final automated acquisition is deemed complete. This preliminary action allows regulatory bodies to verify performance against known standards in advance, reducing approval difficulties by providing pre-validation evidence that the system meets required criteria.
3Measurement precision
If manual ultrasound imaging is performed by highly trained technicians, then accuracy of standardized views is maintained, but productivity and efficiency decrease
Solution Approach 1:
The system enables self-service automated view acquisition where the ultrasound system automatically identifies, classifies, and extracts canonical views without requiring highly trained technicians to manually obtain each standardized view. The AI-driven classification and matching algorithms perform the task autonomously, maintaining accuracy through reference view comparison while dramatically improving productivity by eliminating manual intervention for each view acquisition.
Solution Approach 2:
The system replaces the mechanical skill-based process of manual view acquisition by trained technicians with an automated computational system using AI algorithms. The extractor and comparison modules substitute the manual dexterity and expertise of technicians with algorithmic pattern recognition and image matching, maintaining measurement precision through reference-based validation while increasing productivity through automated high-speed processing.
Data Source
AI summary
An apparatus and method for automatically extracting canonical views from ultrasound imaging data. The apparatus includes at least a processor and a memory communicatively connected to the at least a processor. The memory instructs the processor to receive the ultrasound data from the at least a transducer, classify, using an extractor, a plurality of canonical views, wherein classifying the plurality of canonical views comprises identifying a plurality of initial canonical views, generating a plurality of comparisons of the plurality of initial canonical views to a plurality of reference canonical views, and classifying the plurality of initial canonical views as matching canonical views and non-matching canonical views as a function of the plurality of comparisons, extract a matched canonical view, from the initial canonical views that are classified as matching canonical views as a function of a similarity metric, and display the matched canonical view.