GUI for Biomedical Image Analysis Results Visualization
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Solution Overview
Problem
Current systems for analyzing biomedical images struggle to effectively display and interpret large amounts of information generated by computer-aided detection, making it difficult for pathologists and radiologists to make accurate diagnoses based on subjective criteria.
Innovation Solution
A graphical user interface is developed to display tables, graphs, and plots of image analysis results, allowing users to select and view digital images of tissue slices, with image-based biomarkers generated using object-oriented analysis to predict clinical endpoints by correlating image features with patient health data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If computer-aided detection systems analyze biomedical images to generate diagnostic information, then the quantity and detail of diagnostic data increase, but the difficulty of assimilating and interpreting this information increases
Solution Approach 1:
The graphical user interface divides the complex diagnostic information into multiple organized panes: a first pane displays tables of image analysis results with quantitative metrics, a second pane displays visualized images with highlighted regions, and a third pane displays statistical plots. This segmentation allows pathologists to access different types of information in dedicated spaces rather than being overwhelmed by a single complex display.
Solution Approach 2:
The system transforms multidimensional image analysis data into visual representations across different dimensional spaces. Statistical parameters are plotted in graphical dimensions, image regions are highlighted with visual markers, and correlations are displayed through spatial relationships in plots. This dimensional transformation makes abstract quantitative data perceptually accessible.
2Measurement precision
If computer-aided detection systems perform comprehensive image analysis, then the accuracy of diagnostic data increases, but the time required to process and review the information increases
Solution Approach 1:
The system performs comprehensive image analysis and generates all diagnostic metrics, statistical parameters, and visualizations in advance before the pathologist begins review. The computer-aided detection system pre-processes the images, calculates quantitative features, generates statistical plots, and prepares highlighted images, so that when the pathologist accesses the results, all information is ready for immediate review without requiring additional processing time.
Solution Approach 2:
The system creates visual copies and representations of the diagnostic information in multiple formats simultaneously. The same image data is represented as original images, segmented images with highlighted regions, quantitative tables, and statistical plots. This allows the pathologist to review copied representations rather than analyzing raw data, significantly reducing review time while maintaining accuracy.
3Adaptability or versatility
If subjective criteria are used for diagnosis, then the flexibility in interpreting individual cases increases, but the objectivity and consistency of diagnoses decrease
Solution Approach 1:
The graphical user interface merges objective quantitative data from computer-aided detection with subjective clinical judgment. The system combines numerical measurements, statistical analysis results, and visual image data into a unified display that presents both hard data and visual context, allowing pathologists to make informed subjective decisions based on comprehensive objective information rather than relying on subjective criteria alone.
Data Source
AI summary
A method of intuitively displaying values obtained from analyzing bio-medical images includes displaying a table of the values in a first pane of a graphical user interface. The table contains a user selectable row that includes a reference value and two numerical values. The reference value refers to an image of a tissue slice. The first numerical value is generated by performing image analysis on the image, and the second numerical value indicates a health state of the tissue. The image is displayed in a second pane of the graphical user interface in response to the user selecting the user selectable row. A graphical plot with a selectable symbol associated with the image is displayed in a third pane. The symbol has a position in the plot defined by the values. Alternatively, in response to the user selecting the selectable symbol, the image is displayed in the second pane.