Neural Network X-Ray Orientation Detection and Correction
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Solution Overview
Problem
Portable x-ray imaging often results in incorrectly oriented images due to human error, leading to increased time for physicians to correct orientations, potential degradation of diagnostic systems, and inefficiencies in medical image management within PACS systems.
Innovation Solution
A neural network model-based system for detecting and correcting image orientations, which analyzes x-ray images, assigns orientation classes, and adjusts images to a reference orientation, reducing manual intervention and improving metadata accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual correction of image orientation is performed, then orientation accuracy is improved, but time consumption increases
Solution Approach 1:
The patent replaces the manual mechanical correction process with an automated neural network-based system. The neural network model automatically detects image orientation and applies corrections, eliminating the need for manual intervention while maintaining high accuracy. This substitution of mechanical/manual operations with an intelligent automated system resolves the contradiction between accuracy and time consumption.
Solution Approach 2:
The system enables self-service correction where the neural network automatically identifies and corrects orientation issues without requiring technologist intervention. The model processes images autonomously, detecting orientation errors and applying corrections independently, which dramatically reduces the time required while preserving accuracy through the network's learned capabilities.
2Productivity
If automated neural network correction is implemented, then productivity is improved, but device complexity increases
Solution Approach 1:
The patent introduces a neural network model as an intermediary component between image acquisition and PACS storage. This intermediary automatically performs orientation correction, enabling high productivity without requiring complex manual workflows. The neural network acts as a dedicated processing layer that handles the complexity internally while presenting a simple interface to the overall system.
Solution Approach 2:
The system segments the image processing workflow into distinct functional components: orientation detection, orientation correction, and PACS integration. By dividing the complex task of automated correction into modular segments, the system achieves high productivity through specialized processing while managing overall complexity through clear separation of functions.
3Measurement precision
If manual orientation correction is performed, then metadata accuracy can be maintained, but technologist time is consumed
Solution Approach 1:
The patent replaces manual technologist operations with an automated neural network system that simultaneously corrects both the image orientation and the associated metadata. The neural network automatically updates orientation parameters in the metadata alongside image correction, maintaining accuracy without requiring technologist time for manual verification or adjustment.
Solution Approach 2:
The system performs self-service correction of both image and metadata together. The neural network autonomously identifies orientation errors and applies coordinated corrections to both the image data and its associated metadata, ensuring consistency and accuracy without external intervention. This self-service approach eliminates technologist time consumption while preserving metadata accuracy through the model's learned understanding of proper orientation parameters.
Data Source
AI summary
An x-ray image orientation detection and correction system including a detection and correction computing device is provided. The processor of the computing device is programmed to execute a neural network model that is trained with training x-ray images as inputs and observed x-ray images as outputs. The observed x-ray images are the training x-ray images adjusted to have a reference orientation. The processor is further programmed to receive an unclassified x-ray image, analyze the unclassified x-ray image using the neural network model, and assign an orientation class to the unclassified x-ray image. If the assigned orientation class is not the reference orientation, the processor is programmed to adjust an orientation of the unclassified x-ray image using the neural network model, and output a corrected x-ray image. If the assigned orientation class is the reference orientation, the processor is programmed to output the unclassified x-ray image.