Medical Imaging Data Tagging for Selective Storage
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Solution Overview
Problem
Ultrasound imaging systems face challenges in efficiently managing large amounts of data, particularly in selecting and saving relevant medical images, which is time-consuming and requires significant storage and bandwidth, leading to slowed networking speeds.
Innovation Solution
Implementing a method to automatically classify and tag medical imaging data based on features, allowing for selective saving of images with designated tags, reducing the need for manual selection and minimizing storage and bandwidth requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If manual selection of images to save is performed, then storage space is optimized, but time consumption increases
Solution Approach 1:
The system automatically tags and selects images for saving based on predefined criteria without requiring manual intervention. The classification algorithm independently evaluates each image and determines whether it meets saving criteria, enabling the system to serve itself in the image selection process while optimizing both storage space and time efficiency
Solution Approach 2:
The system changes the parameter of image selection from manual user decision to automated algorithmic decision based on image features. By transforming the selection criterion from subjective user judgment to objective parameter-based classification, the system achieves automatic image filtering that reduces time consumption while maintaining storage optimization
2Loss of information
If all images are saved, then data completeness is improved, but storage requirements increase
Solution Approach 1:
The system extracts only the essential and relevant images from the complete image set based on classification tags. By taking out only those images that meet the saving criteria (such as diagnostic quality, specific anatomical views, or clinically significant findings), the system maintains data completeness for necessary images while eliminating redundant storage of non-essential images
Solution Approach 2:
The system segments the complete image set into different categories based on classification tags (e.g., save-worthy images vs. non-save images). This segmentation allows the system to selectively preserve important images while discarding or archiving less critical ones, thereby reducing overall storage requirements while maintaining completeness of essential diagnostic data
3Speed
If large bandwidth is allocated for data transfer, then data transfer speed is improved, but networking speed for other applications decreases
Solution Approach 1:
The system extracts and transfers only the essential image data that requires saving, rather than transferring all acquired images. By identifying and isolating only the necessary images based on classification tags, the system reduces the total data volume requiring network transfer, thereby maintaining adequate transfer speed for critical data while preserving bandwidth for other networking activities
Solution Approach 2:
The system performs partial data transfer by selecting only a subset of images for saving and transfer rather than transferring the complete image set. This partial action approach ensures that sufficient data transfer speed is allocated to important images while avoiding excessive bandwidth consumption that would impact overall networking performance
Data Source
AI summary
Methods and systems are provided for tagging and selectively saving medical imaging data. One example method includes acquiring medical imaging data with a medical imaging device, tagging a subset of the medical imaging data with a tag based on one or more features of the imaging data, and saving the subset of the imaging data if the tag matches a designated tag.