Ultrasound Image View Classification Using Neural Networks
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current ultrasound examination processes require significant training and time for medical staff to acquire and analyze images, leading to potential errors and re-examinations due to skill-level deviations and the complexity of classifying image views.
Innovation Solution
An artificial neural network-based system is developed to classify and verify ultrasound image views by utilizing metadata from DICOM-formatted images, allowing for the recognition and classification of image views across various ultrasound modes, thereby reducing the reliance on skill level and improving analysis efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If medical staff manually perform ultrasound image acquisition and analysis, then diagnostic accuracy can be maintained through expert judgment, but examination time increases significantly and skill-level deviations cause inconsistencies
Solution Approach 1:
The patent replaces the manual mechanical process of image acquisition and analysis with an automated system using deep learning algorithms. The system automatically performs image segmentation, view classification, and measurement without requiring manual intervention, thereby reducing examination time while maintaining diagnostic accuracy through consistent algorithmic processing.
Solution Approach 2:
The system enables self-service by automatically performing image analysis tasks that previously required skilled medical staff intervention. The automated segmentation and measurement algorithms independently process ultrasound images, eliminating the need for manual analysis and reducing dependency on operator skill levels.
2Adaptability or versatility
If manual image acquisition is performed by medical staff, then flexibility in handling various image views is possible, but classification accuracy decreases due to skill-level deviations
Solution Approach 1:
The patent transforms the classification task from a manual skill-based process to an automated parameter-based system. By using deep learning models trained on diverse ultrasound data, the system achieves consistent classification accuracy across different image views and modes, eliminating variability caused by operator skill levels while maintaining adaptability to various examination scenarios.
3Loss of information
If comprehensive image analysis is performed manually, then detailed diagnostic information can be obtained, but the complexity of the process increases requiring extensive training
Solution Approach 1:
The patent segments the complex image analysis process into distinct automated modules: image segmentation, view classification, and measurement extraction. Each module handles specific tasks independently, reducing overall process complexity while ensuring comprehensive diagnostic information is captured through systematic processing of all image features.
4Measurement precision
If re-examination is performed to correct errors, then measurement accuracy can be improved, but examination time increases and patient burden increases
Solution Approach 1:
The system implements feedback mechanisms where classification results and measurements are automatically verified against established criteria. The automated system provides consistent feedback loops that ensure measurement accuracy without requiring manual re-examination, thereby preventing errors before they occur rather than correcting them afterward.
Data Source
Figure 1
Figure 2A
Figure 2B
AI summary
The present invention provides a method for providing information regarding an ultrasound image view, which is implemented by a processor, and a device using the method, the method comprising the steps of: receiving an ultrasound image of an object; and classifying respective image views on the basis of the received ultrasound image, by using an image view classification model trained to classify a plurality of image views by using an ultrasound image as an input and output respective image views, wherein the respective image views indicate ultrasound image views in a plurality of different ultrasound modes.