Pre-processing Algorithm Selection for Medical Image Visualization
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Solution Overview
Problem
Advanced visualization applications in medical imaging require substantial processing time, leading to delays when starting up, especially when multiple applications are used for a single set of image data.
Innovation Solution
A computer-implemented method using a machine learned model to select a pre-processing algorithm based on image data and metadata, reducing the need for initial processing and speeding up the startup time of advanced visualization applications by recommending or automatically selecting the appropriate pre-processing algorithm.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If advanced visualization applications perform processing of image data upon activation, then the applications can provide full functionality, but the startup time increases significantly
Solution Approach 1:
The system performs pre-processing of image data before the advanced visualization application is activated. A pre-processing application analyzes the image data and prepares it in advance, so that when the user launches the visualization application, the heavy computational work has already been completed or partially completed, significantly reducing startup time while maintaining full functionality.
2Adaptability or versatility
If multiple advanced visualization applications are used for a single set of image data, then more comprehensive analysis is achieved, but the total processing time increases
Solution Approach 1:
The pre-processing application performs preparatory computations on the image data before any visualization applications are launched. This preliminary processing creates a prepared dataset that can be efficiently used by multiple subsequent visualization applications, eliminating redundant processing and reducing the total time required for comprehensive multi-application analysis.
Solution Approach 2:
The system merges the pre-processing step as a shared preliminary operation that serves multiple visualization applications. Instead of each application performing its own independent processing, the pre-processing application combines the preparatory work into a single phase that benefits all subsequent applications, reducing overall processing time.
3Productivity
If a specific pre-processing algorithm is selected for an advanced visualization application, then processing efficiency is improved, but the complexity of algorithm selection increases
Solution Approach 1:
The pre-processing application automatically selects and executes the appropriate pre-processing algorithm based on the characteristics of the image data, without requiring manual intervention from the user. The system serves itself by making intelligent algorithm selection decisions, thereby improving processing efficiency while avoiding the complexity of user-facing algorithm selection interfaces.
Solution Approach 2:
The pre-processing application acts as an intermediary between the raw image data and the visualization applications. It automatically determines which pre-processing algorithm is most suitable for the given data and performs the appropriate processing, mediating the complexity of algorithm selection away from the user and delivering optimized results to the visualization applications.
Data Source
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AI summary
A computer-implemented method (40) of reducing processing time of an application for visualizing image data, the application being one of a plurality of applications selectable by a user and each of the plurality of applications comprising a pre-processing algorithm for pre-processing the image data. The method comprises predicting which one of the pre-processing algorithms is to be performed in response to a selection of an application by the user by: - extracting a feature vector from the image data, metadata, and/or additional data associated with the image data, - supplying the feature vector as input to a machine learned model, and - receiving an algorithm identifier as output from the machine learned model, the algorithm identifier identifying the pre-processing algorithm, the method further comprising: - using the algorithm identifier to select (42) the pre-processing algorithm, thereby obtaining a selected pre-processing algorithm, and - pre-processing (43) the image data using the selected pre-processing algorithm.