Medical Image Slice Annotation Using Similarity-Based Reference Matching
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Solution Overview
Problem
Medical imaging data analysis is time-consuming for radiologists, particularly in emergency cases, due to the need to navigate through numerous slices to identify specific features, and existing landmark detection methods are computationally expensive and modality-dependent.
Innovation Solution
A method that annotates medical imaging data slices with words corresponding to their features by comparing them to a reference dataset using similarity metrics, allowing efficient and flexible annotation independent of imaging modality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If radiologists manually navigate through numerous imaging slices to identify specific features, then diagnostic accuracy is maintained, but time consumption increases significantly
Solution Approach 1:
The system performs preliminary annotation of imaging slices by automatically detecting features and assigning descriptive words to each slice before radiologist review. This pre-processing step creates an indexed database of annotated slices that can be quickly searched and retrieved, eliminating the need for radiologists to manually navigate through all slices while preserving diagnostic accuracy through maintained annotation quality
2Difficulty of detecting and measuring
If landmark detection methods are used to identify features in medical images, then feature detection capability is improved, but computational cost and modality dependency increase
Solution Approach 1:
The system replaces expensive, complex landmark detection algorithms with a simpler, more efficient annotation approach using trained models that generate feature descriptions through comparative analysis. This substitute method achieves comparable or superior feature detection capability while significantly reducing computational requirements and eliminating modality dependency
Solution Approach 2:
The invention substitutes traditional mechanical landmark detection methods with a data-driven annotation system that uses trained models to compare imaging slices against reference data. This replacement transitions from complex algorithmic processing to a more efficient similarity-based annotation approach that reduces computational burden while maintaining detection accuracy
3Ease of operation
If comprehensive annotation of all imaging slices is performed, then searchability and accessibility are improved, but processing time and computational resources increase
Solution Approach 1:
The system performs annotation on a selective basis, processing imaging slices in batches or prioritizing certain slices based on clinical relevance, patient condition, or preliminary analysis results. This partial annotation approach provides sufficient searchability for clinical workflows without the need to annotate every single slice, thereby maintaining processing speed while delivering adequate accessibility
Data Source
AI summary
There is disclosed a method and apparatus for annotating a first portion of medical imaging data with one or more words corresponding to a respective one or more features represented in the first portion of medical imaging data. A similarity metric indicating a degree of similarity between the first portion and each of a plurality of second portions of reference medical imaging data is determined, at least one of the plurality of second portions being annotated with one or more first words corresponding to a respective one or more features represented in the second portion. A second portion is selected based on the similarity metrics, and the first portion is annotated with the one or more first words with which the second portion, selected for the first portion, is annotated.