Automated Medical Image View Generation via Diagnosis Mapping
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Solution Overview
Problem
The manual generation of medical image views for diagnosing patients is time-consuming and inefficient, requiring clinicians to manually set contrast, brightness, and other parameters to confirm or reject suspected diagnoses.
Innovation Solution
A system that maps suspected diagnoses to sets of viewing parameters, using a database and transformer to generate image-specific views automatically, reducing manual effort and improving consistency by using image models and shape models to adapt parameters to the medical image content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual view generation is used to diagnose patients, then the clinician can confirm or reject suspected diagnoses by setting contrast, brightness, and other parameters, but the process becomes time-consuming and inefficient
Solution Approach 1:
The system enables self-service by automatically generating optimal views based on suspected diagnoses without requiring manual clinician intervention. The automated view generator analyzes the medical image data and produces appropriate views with correct contrast, brightness, and geometric parameters, allowing the system to serve itself rather than relying on manual operations
Solution Approach 2:
The system performs preliminary action by pre-defining viewing parameters associated with different suspected diagnoses in a database. When a diagnosis is suspected, the corresponding viewing parameters are automatically retrieved and applied, eliminating the need for time-consuming manual parameter adjustment during the diagnostic process
2Reliability
If manual view generation is used, then the clinician can adjust parameters to confirm or reject diagnoses, but the process lacks consistency
Solution Approach 1:
The system applies parameter changes by automatically adjusting viewing parameters (contrast, brightness, geometric parameters) based on the suspected diagnosis. The database stores pre-defined parameter sets for different diagnostic scenarios, and the system retrieves and applies the appropriate parameters, ensuring consistent and reliable view generation across different cases and clinicians
3Loss of information
If 3D medical image data is processed to create cross-sectional views or 3D rendered views, then the necessary image findings can be obtained, but the manual steps become very time-consuming
Solution Approach 1:
The system replaces the mechanical manual process of view generation with an automated computational system. The automated view generator uses algorithms to process 3D medical image data and generate cross-sectional views or 3D rendered views, substituting manual mechanical operations with automated electronic processing that is both faster and equally effective at producing necessary image findings
Data Source
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AI summary
A system for generating a view of a medical image comprises an input (1) for receiving information indicative of a suspected diagnosis of a patient. An input (2) for receiving a medical image of the patient. A mapper (3) for mapping the suspected diagnosis of the patient to a set of viewing parameters for viewing the medical image. A view generator (8) for providing a view of the medical image in accordance with the set of viewing parameters. A database (4) for mapping a suspected diagnosis into a set of generic viewing parameters. A transformer (5) for transforming the set of generic viewing parameters into a set of image-specific viewing parameters based on content of the medical image.