Neural Network Window Estimation for Medical Imaging
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Solution Overview
Problem
Current methods for adjusting window-level settings in diagnostic images, such as CT images, often require manual intervention and may not adequately detect subtle differences, leading to missed diagnoses, especially in cases like malignancy or stroke detection.
Innovation Solution
A deep learning-based approach that estimates optimal window settings for each input image using a convolutional neural network, which simultaneously trains a window estimation module and a classification network to improve the detection of lesions and normal regions by adjusting brightness and contrast, and combines predictions from multiple window settings for enhanced accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual window setting adjustment is used, then radiologists can detect abnormalities, but the process requires significant manual time and may miss subtle differences
Solution Approach 1:
The system performs self-service by automatically estimating optimal window settings through a trained neural network that analyzes input images and outputs appropriate window/level parameters without requiring manual radiologist adjustment, thereby eliminating manual time while maintaining detection accuracy
Solution Approach 2:
The neural network performs preliminary action by pre-estimating optimal window settings before the radiologist views the image, automatically adjusting brightness and contrast parameters in advance to highlight potential abnormalities, thus saving manual adjustment time while improving detection precision
2Productivity
If default window settings are used, then images can be displayed quickly, but subtle abnormalities may not be detected
Solution Approach 1:
The system applies dynamics by transitioning from static default window settings to dynamic, image-specific window settings estimated by the neural network, allowing each image to receive customized brightness and contrast parameters that optimize abnormality detection while maintaining quick display speed
Solution Approach 2:
The neural network changes parameters by automatically adjusting window width and window level values based on the specific characteristics of each input image, transforming fixed default settings into adaptive parameters that enhance detection sensitivity without sacrificing display speed
3Measurement precision
If multiple window settings are manually evaluated, then detection sensitivity improves, but the complexity of the process increases
Solution Approach 1:
The system replaces the mechanical process of manual window setting evaluation with an automated neural network-based estimation system that processes images and generates optimal settings automatically, reducing process complexity while maintaining or improving detection sensitivity through multiple setting evaluations
Data Source
AI summary
This invention relates to estimating the window width and window level (center) which are typically used to view and then transform diagnostic imaging data to grayscale images. These grayscale images are then used to check the presence of diseases or abnormalities. For each individual diagnostic image, this invention automatically estimates the most appropriate values. This automatic estimation is done by a specialized module added on to a convolutional neural network-based disease detection system.