Medical Image Annotation Tool with Smart Organ Shape Palette
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Solution Overview
Problem
The training of deep learning models for medical applications requires large amounts of high-quality annotated medical images, which is time-consuming and costly due to the scarcity of highly trained specialists, leading to variability and inaccuracies when non-specialists are used for annotation.
Innovation Solution
A method and system that utilize a smart organ shape palette to pre-set high-quality annotations by specialists into shapes within an icon library, allowing non-specialists to efficiently annotate medical images by automatically adjusting selected icons to match anatomical features and enabling manual adjustments, thereby standardizing and refining annotations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If highly trained specialists annotate medical images, then annotation accuracy is improved, but time consumption and cost increase
Solution Approach 1:
The system pre-loads annotation icons and organ shapes into a palette based on the current image type and anatomical region, so that when annotation is needed, the appropriate tools are already prepared and immediately available, eliminating the time specialists would otherwise spend selecting and configuring annotation tools
Solution Approach 2:
The system creates template annotations from specialist-annotated images and stores them as reusable icons in a library. Non-specialists can then copy these pre-defined annotation patterns and apply them to similar images, achieving specialist-level accuracy without requiring specialist time investment for each individual annotation
2Reliability
If highly trained specialists annotate medical images, then annotation quality is improved, but cost increases
Solution Approach 1:
The system creates template annotations from specialist-annotated images and stores them as reusable icons in a library. Non-specialists can then copy these pre-defined annotation patterns and apply them to similar images, achieving specialist-level accuracy without requiring specialist time investment for each individual annotation
Solution Approach 2:
The system automatically determines the appropriate annotation icon and organ shape based on the current image and selected tool, eliminating the need for specialists to manually configure each annotation. The system serves itself by auto-adjusting annotations to match anatomical features, reducing reliance on expensive specialist labor
3Productivity
If non-specialists annotate medical images, then time consumption is reduced, but annotation consistency and accuracy deteriorate
Solution Approach 1:
The system creates template annotations from specialist-annotated images and stores them as reusable icons in a library. Non-specialists can then copy these pre-defined annotation patterns and apply them to similar images, achieving specialist-level accuracy without requiring specialist time investment for each individual annotation
Solution Approach 2:
The system automatically adjusts annotation parameters such as organ shape, size, and position based on the selected icon and current image characteristics. This auto-adjustment ensures that annotations maintain consistent anatomical accuracy across different users, transforming the annotation process from manual parameter setting to automated parameter optimization
Data Source
AI summary
Various methods and systems are provided for suggesting annotation shapes to be applied to a medical image. In one example, a method includes outputting, for display on a display device, a set of annotation icons from an icon library based on a current image displayed on the display device and displaying an annotation on the current image in response to selection of an annotation icon from the set. The method further includes automatically adjusting the annotation to a corresponding anatomical feature in the current image and saving the current image with the annotation.