Medical Image Annotation Display Under Resource Constraints
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Solution Overview
Problem
The large size and complexity of medical image data and associated annotations pose challenges for visualization on resource-limited display devices, leading to slow loading, sluggish response, and susceptibility to crashes.
Innovation Solution
A method that optimizes the display of medical image data by adapting the complexity of annotations based on the display device's computational resources, generating a subset of annotation data that is computationally efficient and visually simplified, allowing visualization on devices like smartphones and tablets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If the entirety of the medical image data and all annotations is loaded into memory for display, then complete annotation information is available for visualization, but the computational resources required become extremely intensive, causing slow loading, sluggish response, and susceptibility to crashes
Solution Approach 1:
The patent extracts and displays only a selected subset of annotation data that is most relevant to the current viewing context, rather than loading all annotation information into memory. This selective extraction approach maintains display device stability while preserving the essential diagnostic information needed for clinical work.
Solution Approach 2:
The annotation data is segmented into multiple subsets based on relevance, priority, and contextual importance. The system loads and displays only the necessary segments (e.g., annotations within the current field of view, high-priority findings), while keeping other segments available but unloaded, thus reducing memory consumption and improving system reliability.
2Loss of information
If the entirety of the medical image data and all annotations is loaded into memory for display, then complete annotation information is available for visualization, but the loading speed and response time become extremely slow
Solution Approach 1:
The system extracts only the essential annotation data needed for the current display context, excluding redundant or low-priority annotations. This extraction strategy dramatically reduces the amount of data that needs to be loaded and processed, thereby improving loading speed and response time while maintaining the completeness of diagnostically relevant information.
Solution Approach 2:
Annotation data is pre-processed and organized into priority levels and contextual groups before display. This preliminary organization allows the system to quickly retrieve and load only the necessary subsets of annotations based on the current viewing context, avoiding the need to process and filter large amounts of data in real-time, thus improving loading speed.
3Productivity
If the complexity of annotations is reduced to improve processing speed, then the computational load decreases, but the detail and precision of annotation information may be lost
Solution Approach 1:
The patent applies different levels of annotation detail and complexity to different regions and contexts within the medical image. High-precision, detailed annotations are provided for regions of interest and areas requiring close examination, while simplified annotations are used for peripheral or less critical areas. This local differentiation maintains annotation precision where needed while improving overall processing speed.
Solution Approach 2:
The annotation complexity and detail level are dynamically adjusted based on the current viewing context, zoom level, and user interaction. When a user zooms in or focuses on a specific region, the system dynamically loads and displays more detailed annotations for that region, while maintaining simplified views elsewhere, thus balancing processing speed with annotation precision adaptively.
4Use of energy by moving object
If resource constraint parameters are used to limit the number of annotation values, then the computational resources required are reduced, but the amount of annotation information available for display decreases
Solution Approach 1:
The system introduces an intermediary selection layer that intelligently filters and prioritizes annotation data based on clinical relevance, spatial context, and user preferences. This intermediary layer acts as a mediator between the full annotation dataset and the display device, selecting and presenting the most important annotation information while reducing the overall data volume, thus maintaining annotation information availability while reducing computational resource usage.
Data Source
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AI summary
A method for processing medical image data for a display device, the method comprising: receiving medical image data, the medical image data comprising at least one image and initial annotation data corresponding to at least one image, the initial annotation data being defined by a first plurality of values; receiving a set of resource constraint parameters; receiving instructions to select a subregion of the image as a display subregion; generating display annotation data to represent the initial annotation data, the display annotation data corresponding to the display subregion and being defined by a second plurality of values, wherein the second plurality is generated from the first plurality and the number of values in the second plurality is determined by the resource constraint parameters; and providing instructions to display the display subregion overlaid with the display annotation data on the display device.