Medical Image Contrast Mapping for 3D Slice Feature Visibility
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Solution Overview
Problem
Existing methods for adjusting contrast settings in 2D slices of 3D medical images require manual intervention, which slows down the diagnostic process and increases professional fatigue, potentially leading to delayed diagnoses.
Innovation Solution
An automated method for determining and applying target contrast settings to each 2D slice of a 3D image, using machine-learning algorithms and historical data to identify and enhance the visibility of specific anatomical features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual adjustment of contrast settings is used, then the medical professional can control the appearance of anatomical features, but the diagnostic process slows down and professional fatigue increases
Solution Approach 1:
The system automatically determines and applies optimal contrast settings for each 2D slice without requiring manual intervention from the medical professional. The automated contrast determination system analyzes the image data and applies appropriate contrast settings autonomously, eliminating the manual adjustment process while maintaining diagnostic quality
Solution Approach 2:
Contrast settings are determined in advance for each 2D slice before the medical professional views the images. The system pre-processes the 3D medical image data to identify target anatomical features and calculate optimal contrast parameters, so that when the professional reviews the slices, the contrast is already optimized and no adjustment is needed
2Speed
If predefined contrast settings are used, then the initial display is quick, but the settings often need manual adjustment to suit specific situations
Solution Approach 1:
The system applies different contrast settings to different regions within each 2D slice based on the local anatomical features present. Rather than using a single predefined contrast setting for the entire image, the automated system identifies target regions containing anatomical features and applies optimized contrast parameters specifically to those regions, allowing each area to be displayed with appropriate contrast for its specific content
3Measurement precision
If manual contrast adjustment is required for each 2D slice, then optimal visibility of target features can be achieved, but time is lost repeatedly adjusting settings
Solution Approach 1:
The automated contrast determination system uses feedback from image analysis to optimize contrast settings. The system analyzes each 2D slice to identify target anatomical features, evaluates the current contrast performance, and automatically adjusts contrast parameters to maximize feature visibility. This closed-loop approach ensures optimal visibility without manual intervention
Solution Approach 2:
The manual mechanical process of adjusting contrast sliders and buttons is replaced with an automated computational system. The automated contrast determination uses image processing algorithms and machine learning models to calculate optimal contrast settings, substituting the manual mechanical adjustment process with an automated digital system that operates without human intervention
Data Source
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AI summary
A method and system for preferentially displaying 2D slices of a 3D medical image according to determined contrast settings. The method comprises generating a file that contains a plurality of target contrast settings and a plurality of position indicators, each associated with a 2D slice of the 3D image. Each target contrast setting enhances the appearance of a target anatomical feature in the associated 2D slice, while each position indicator describes the position of the target anatomical feature in the associated 2D slice. The generated file is then provided to a user interface system configured to display the 2D slices of the 3D image according to the information in the file. Specifically, in the displayed 2D slice, a respective target contrast setting is applied to at least a target region in the 2D slice that contains the target anatomical feature, as determined from a respective position indicator.