Automated Image Annotation Correction System for Medical Imaging
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Solution Overview
Problem
Current automated image annotation systems in medical imaging face challenges in ensuring user repeatability and reproducibility, leading to variability in image correction that can impact patient care decisions.
Innovation Solution
A computer-based system that generates alternative image annotations using multiple algorithms, assigns similarity scores, and groups them for user selection, reducing variability by presenting representative annotations for validation and correction, thereby improving reproducibility and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual tools are used for correcting automated image annotations, then user flexibility and correction capability are improved, but inter-user and intra-user variability increases
Solution Approach 1:
The system introduces an intermediary layer between manual user correction and the final annotation result. Multiple algorithms generate alternative annotations that serve as intermediaries, guiding users toward more consistent corrections while preserving user control. This mediator reduces variability by constraining user choices to pre-computed alternatives rather than allowing completely free manual correction.
Solution Approach 2:
The system generates multiple copies (alternative annotations) of the correction process through different algorithms. Instead of relying on a single manual correction that varies by user, the system creates multiple algorithmic copies of potential corrections, then presents these as options to the user. This copying approach ensures consistency while maintaining user involvement.
2Measurement precision
If multiple algorithms are used to generate alternative annotations, then annotation accuracy and reproducibility are improved, but system complexity increases
Solution Approach 1:
The system segments the annotation task into multiple independent algorithmic components, each generating alternative annotations. Rather than using one complex monolithic system, the annotation process is divided into separate algorithmic segments that can be independently developed, tested, and combined. This segmentation manages complexity while improving accuracy through diverse algorithmic approaches.
Solution Approach 2:
The system merges multiple algorithmic outputs into a unified set of alternative annotations. Different algorithms are combined in a way that their results complement rather than conflict with each other. The merging process integrates diverse algorithmic strengths while presenting a cohesive interface to the user, managing complexity through systematic integration.
3Reliability
If users are presented with multiple alternative annotations for selection, then user variability is reduced and reproducibility is improved, but user interaction time increases
Solution Approach 1:
The system applies partial action by generating a limited set of curated alternative annotations rather than presenting all possible corrections. Instead of exhaustively exploring every possible annotation variation, the system generates a selective subset of meaningful alternatives that are most likely to improve reproducibility. This partial approach balances comprehensiveness with efficiency.
Solution Approach 2:
The system performs preliminary action by pre-computing multiple alternative annotations before user interaction. Rather than generating alternatives in real-time during user review, the system prepares the alternative annotation set in advance, allowing users to select from pre-processed options. This preliminary computation reduces user interaction time while maintaining reproducibility benefits.
Data Source
AI summary
Systems and methods are disclosed for controlling image annotation. One method includes acquiring a digital representation of image data and generating a set of image annotations for the digital representation of the image data. The method also may include determining an association between members of the set of image annotations and generating one or more groups of members based on the association. A representative annotation from the one or more groups may also be determined, presented for selection, and the selection may be recorded in memory.