Identifying auto-fluorescent artifacts in a multiplexed immunofluorescent image
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing image analysis methods for multiplexed immunofluorescent images struggle with detecting auto-fluorescent artifacts such as red blood cells, fat, and tissue, leading to inaccurate detection and segmentation of target regions due to overlapping intensity distributions, resulting in over-detection, under-detection, or misclassification.
Innovation Solution
A machine-learning model, specifically a U-Net architecture, is trained to identify and predict the locations of auto-fluorescent artifacts in multiplexed immunofluorescent images, allowing for adjustments in subsequent image processing to improve accuracy and efficiency in tumor detection and analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional image analysis methods are used to detect biomarkers in multiplexed immunofluorescent images, then the detection process is simple and fast, but the detection precision deteriorates due to overlapping intensity distributions between auto-fluorescent artifacts and target regions
Solution Approach 1:
The patent applies segmentation by dividing the image analysis into distinct stages: first identifying auto-fluorescent artifacts, then segmenting target regions excluding those artifacts. This multi-stage segmentation approach resolves the contradiction by improving detection precision through systematic separation of artifact and target detection processes
Solution Approach 2:
The patent introduces an intermediary classification step that identifies auto-fluorescent artifacts as a separate category before final target detection. This intermediary process acts as a mediator between raw image data and final biomarker detection, improving precision by filtering out confounding artifacts while maintaining manageable analysis complexity through modular processing
2Reliability
If conventional image analysis methods are used without artifact identification, then the processing speed is fast, but the reliability deteriorates due to over-detection, under-detection, or misclassification of target regions
Solution Approach 1:
The patent applies preliminary action by performing auto-fluorescent artifact identification before final target region detection. This preliminary step improves detection reliability by pre-filtering confounding elements, while the automated nature of the preliminary classification minimizes additional processing time through efficient algorithms
Solution Approach 2:
The system performs self-service by automatically identifying and flagging auto-fluorescent artifacts without requiring manual intervention or complex post-processing. This automated self-correction mechanism improves reliability by eliminating human error while maintaining processing efficiency through algorithmic automation
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The model effectively distinguishes auto-fluorescent artifacts from target regions, enhancing the accuracy of tumor detection and characterization by reducing false positives and improving image segmentation.
Implementation Method 1
a given type of biological material may naturally auto-fluoresce at a given frequency
Data Source
AI summary
Embodiments disclosed herein generally relate to identifying auto-fluorescent artifacts in a multiplexed immunofluorescent image. Particularly, aspects of the present disclosure are directed to accessing a multiplexed immunofluorescent image of a slice of specimen, wherein the multiplexed immunofluorescent image comprises one or more auto-fluorescent artifacts, processing the multiplexed immunofluorescent image using a machine-learning model, wherein an output of the processing corresponds to a prediction that the multiplexed immunofluorescent image includes one or more auto-fluorescent artifacts at one or more particular portions of the multiplexed immunofluorescent image, adjusting subsequent image processing based on the prediction, performing the subsequent image processing, and outputting a result of the subsequent image processing, wherein the result corresponds to a predicted characterization of the specimen.