Metadata-Based Anatomy Recognition for Efficient Medical Image Routing
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Solution Overview
Problem
Large medical organizations face challenges in routing medical data between different systems due to the large size of medical images and varying metadata formats, leading to inefficient processing and resource usage, particularly in identifying and routing relevant anatomical views for clinical software applications.
Innovation Solution
A metadata-based anatomy recognition system that uses a model to infer anatomical classifications from metadata, such as DICOM tags, to improve the routing of medical images to appropriate clinical applications, reducing unnecessary processing and resource usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If pixel data is transferred to infer anatomy, then anatomy identification accuracy is improved, but data transfer volume and processing time increase significantly
Solution Approach 1:
The patent extracts only the essential metadata elements (DICOM tags containing anatomical information) from the complete medical image data, separating the anatomy identification function from the full pixel data transfer. This allows anatomy inference to proceed with minimal data extraction rather than transferring entire image datasets.
Solution Approach 2:
The patent segments the medical image data into distinct components: metadata (DICOM tags) and pixel data. By identifying and processing only the metadata segment for anatomy inference, the system achieves accurate anatomy identification without the overhead of transferring and processing complete pixel data sets.
2Reliability
If complete studies are transferred for processing, then clinical application processing completeness is improved, but network traffic and processing overhead increase
Solution Approach 1:
The system extracts and transfers only the essential metadata components required for anatomy identification and clinical application routing, rather than transferring complete medical studies. This selective extraction maintains processing reliability by providing sufficient anatomical information while dramatically reducing network traffic volume.
3Adaptability or versatility
If metadata is manually input, then flexibility in metadata format is improved, but metadata reliability decreases due to errors and inconsistencies
Solution Approach 1:
The patent introduces an intermediary anatomy recognition model that processes metadata from multiple sources and standardizes it into consistent anatomical classifications. This intermediary layer mediates between diverse manual input formats and the requirements for reliable, consistent metadata, improving accuracy while maintaining format flexibility through the modeling process.
4Measurement precision
If pixel-based anatomy recognition is used, then anatomy identification accuracy is improved, but computational complexity and processing time increase
Solution Approach 1:
The patent extracts the essential anatomical information from complex pixel data into simplified metadata representations. By working with these extracted metadata features rather than raw pixel data, the system maintains anatomy identification accuracy while significantly reducing computational complexity and processing requirements.
Solution Approach 2:
The system segments the anatomy recognition task into two stages: first extracting anatomical information into metadata during image acquisition, then performing recognition on the segmented metadata rather than processing complete pixel data. This segmentation reduces computational complexity while preserving identification accuracy.
Data Source
AI summary
Provided are computer-implemented systems and methods for metadata-based anatomy recognition and computer-implemented systems and methods for generating a model for metadata-based anatomy recognition. The metadata-based anatomy recognition includes: providing, in a memory in communication with a processor, a model for metadata-based anatomy recognition; receiving, using a network device in communication with the processor, at least one medical image object comprising a plurality of metadata; determining, at the processor, a predicted anatomy classification associated with the at least one medical image object based on the model for metadata-based anatomy recognition and the plurality of metadata; and storing, in the memory, the predicted anatomy classification in association with the at least one medical image object in a database.