Bidirectional Inference for Medical Image Annotation
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Solution Overview
Problem
The manual annotation of Regions of Interest (RoI) in large volumes of medical imaging data is highly time-consuming and prone to inconsistencies due to variations among annotators, especially in serialized imaging like CT and MRI scans.
Innovation Solution
A medical imaging annotation device and method that utilize a bidirectional inference mechanism to generate final annotation labels on intermediate slice images based on manual annotations from the first and last slice images of serialized medical images, leveraging a tracking model and a merger to enhance annotation accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual annotation is performed on each slice individually, then annotation accuracy can be maintained, but the time consumption increases significantly
Solution Approach 1:
The system performs preliminary actions by annotating only the first and last slices manually, then uses bidirectional inference to automatically generate annotations for all intermediate slices. This preliminary manual annotation of boundary slices provides the foundation for automated annotation propagation through the entire image stack, significantly reducing the total annotation time while maintaining accuracy.
Solution Approach 2:
The bidirectional inference mechanism enables the system to annotate itself by using the manually annotated first and last slices as seeds to automatically generate annotations for intermediate slices. The system leverages the spatial relationships and anatomical continuity within the medical image stack to self-annotate without requiring manual intervention for each individual slice.
2Quantity of substance
If manual annotation is performed by multiple annotators, then comprehensive coverage can be achieved, but inconsistencies and variability increase
Solution Approach 1:
The bidirectional inference mechanism provides feedback by propagating annotations from both the first and last slices toward the center of the image stack. This dual-directional feedback loop allows the system to cross-validate annotations and resolve inconsistencies that may arise from single-annotator variability, thereby improving reliability while maintaining comprehensive coverage.
Solution Approach 2:
The system merges the annotation information from the first slice and the last slice through bidirectional inference to create a unified annotation set for all intermediate slices. By combining the annotation seeds from both boundaries and integrating them through the inference mechanism, the system achieves consistent annotations that reflect the consensus of both boundary references.
3Productivity
If automated annotation is applied to all slices, then productivity increases, but annotation accuracy may decrease due to lack of manual verification
Solution Approach 1:
The system performs preliminary manual annotation of the first and last slices to establish accurate annotation seeds before applying automated bidirectional inference to intermediate slices. This preliminary action ensures that the automated process has reliable starting points, maintaining high accuracy while achieving high productivity through automation of the majority of slices.
Data Source
AI summary
A medical imaging annotation device includes an interface, a memory and a processor. The memory is configured to store serialized medical images. The processor is coupled to the interface and the memory. The processor is configured to receive, via the interface, a first manual annotation on a first slice image of the serialized medical images and a second manual annotation on a second slice image of the serialized medical images. The processor is configured to execute a bidirectional inference mechanism to generate final annotation labels on intermediate slice images of the serialized medical images based on the first manual annotation and the second manual annotation respectively.