2D Medical Image Analysis with 3D Registration for Device Detection
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Solution Overview
Problem
The challenge in analyzing 2D medical images is the projection of extracorporeal devices that obscure internal tissue structures, complicating image interpretation, and existing methods require large training datasets or high annotation complexity.
Innovation Solution
A method that combines 2D medical image data with additional image data from a different modality to perform a separate analysis, allowing for the identification and localization of extracorporeal devices using artificial intelligence, reducing the need for extensive training data and annotation efforts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Difficulty of detecting and measuring
If deep learning is used to detect and identify extracorporeal devices in 2D medical images, then detection capability is improved, but large training datasets are required which increases data requirements and annotation complexity
Solution Approach 1:
The patent transitions from analyzing only 2D medical images to incorporating 3D image data from CT or MRI scans. This dimensional change provides additional spatial information that helps AI models detect and differentiate extracorporeal devices more effectively, reducing the reliance on large 2D training datasets while improving detection accuracy.
Solution Approach 2:
The patent introduces a registration process that aligns 3D image data with 2D medical images as an intermediary step. This registration acts as a mediator that integrates multi-modal data, enabling the system to leverage 3D structural information to enhance 2D image analysis without requiring extensive 2D training data.
2Measurement precision
If segmentation of external and inserted objects is performed to improve image analytics, then analysis quality is improved, but annotation complexity increases significantly
Solution Approach 1:
By incorporating 3D image data, the system can perform segmentation in three dimensions rather than two. This provides additional contextual information about object boundaries and relationships, improving segmentation quality while reducing the annotation burden since 3D spatial relationships are inherently more descriptive than 2D projections.
Solution Approach 2:
The patent performs registration of 3D and 2D data as a preliminary action before segmentation. This pre-alignment provides a structured framework that simplifies subsequent segmentation tasks, reducing the complexity of annotations required while maintaining high analysis quality.
3Quantity of substance
If synthetic cases are created to avoid large training datasets, then data requirements are reduced, but mismatch between training and test conditions occurs due to concept drift
Solution Approach 1:
The use of real 3D image data from CT or MRI scans provides authentic anatomical and device information that closely matches actual test conditions. This dimensional enhancement allows the system to train on diverse real-world 3D cases without relying on synthetic data, thereby maintaining reliability and avoiding concept drift while still reducing the need for large 2D training datasets.
Data Source
AI summary
A method for automatically analysing 2D medical image data, including an additional object comprises acquiring the 2D medical image data from an examination portion of a patient using a first modality; acquiring additional image data from the examination portion using a different modality; and performing an automatic image analysis based on the acquired 2D medical image data and the acquired additional image data, the image analysis being adapted to the additional object.