Virtual Staining for Lesion Detection Accuracy
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Solution Overview
Problem
Current image diagnosis technologies face challenges in accurately detecting abnormal tissues or cells from images stained with a single staining method, leading to false detection or detection failure, and the process becomes costly when multiple staining methods are involved.
Innovation Solution
An image diagnosis assisting apparatus and method that calculates and estimates feature amounts from images stained with one type of staining method, allowing for the determination of abnormal tissues or cells, and generates images with different staining components to improve detection accuracy and reduce costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple staining methods are used to improve detection accuracy, then detection reliability is improved, but inspection cost increases
Solution Approach 1:
The patent creates a virtual image with a different staining appearance from the original single-staining image through image processing. This virtual copy simulates what the tissue would look like if stained with a different method, allowing the system to achieve multi-staining detection accuracy without the cost of actual multiple staining procedures. The processing unit generates this virtual stained image by transforming the visual characteristics of the original image.
Solution Approach 2:
The patent changes the visual parameters of the image through processing to simulate different staining effects. By adjusting color, contrast, and other visual parameters of the digital image, the system creates a virtual representation that appears as if stained by a different method, thereby obtaining multiple detection perspectives from a single physical staining sample.
2Reliability
If multiple staining methods are used to prevent false detection, then detection reliability is improved, but the complexity of the process increases
Solution Approach 1:
Instead of performing multiple physical staining procedures, the system creates a virtual copied image with different staining characteristics through digital processing. This approach maintains detection reliability by providing multiple visual perspectives while avoiding the operational complexity of multiple staining workflows, sample handling steps, and coordination of multiple staining protocols.
Solution Approach 2:
The patent replaces the mechanical and chemical process of multiple physical stainings with an information processing approach. The complex wet laboratory procedures are substituted by computational image processing that transforms the digital representation of the tissue, thereby reducing procedural complexity while maintaining or improving detection reliability.
3Quantity of substance
If only one staining method is used to reduce cost, then inspection cost is reduced, but detection precision deteriorates
Solution Approach 1:
The system creates a virtual image that simulates the appearance of different staining methods from the single actual staining image. This virtual copy provides additional diagnostic information and alternative visual perspectives that improve detection precision, effectively giving the benefits of multiple stainings while using only one physical staining procedure.
Solution Approach 2:
The patent adds an informational dimension by generating a virtual image with different staining characteristics. This creates an additional analytical dimension from the same physical sample, allowing pathologists to evaluate tissue features from multiple visual perspectives without requiring multiple physical samples or stainings, thereby improving precision while controlling costs.
Data Source
AI summary
An image diagnosis assisting apparatus according to the present invention executes: processing of inputting an image of a tissue or cell; processing of extracting a feature amount of a tissue or cell from a processing target image; processing of extracting a feature amount of a tissue or cell from an image having a component different from that of the target image; and processing of determining presence or absence of a lesion and lesion probability for each of the target images by using a plurality of the feature amounts.