Microscopy Object Capture Across Contrasts for Missed Transfections
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Solution Overview
Problem
Conventional transfection methods in microscopy often result in missed transfections, where cells do not take on markers, leading to non-visible cells and incorrect marking of unintended regions, which affects the quality of training models and applications like virtual staining, segmentation, and lineage tracing.
Innovation Solution
A method using image recognition and machine learning, specifically a one-class classifier, to identify and differentiate between transfected and non-transfected cells by comparing images with different contrasts, allowing for automatic detection and transfer of cell positions across contrasts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If transfection with marker is performed to improve visibility, then optical identifiability of cells is improved, but some cells remain invisible due to missed transfection
Solution Approach 1:
The patent combines multiple image channels (fluorescence channel with marker and brightfield channel without marker) to simultaneously detect both transfected and non-transfected cells. The one-class classifier processes both channels to identify cell positions, merging the information from both sources to achieve complete cell detection regardless of transfection status.
Solution Approach 2:
The patent introduces a one-class classifier as an intermediary computational tool that mediates between the two image channels. This classifier learns the characteristics of cells from the brightfield channel and uses this knowledge to identify cells in the fluorescence channel, serving as a bridge to detect both transfected and non-transfected cells.
2Measurement precision
If dye is introduced to mark cells, then visibility of cell components is improved, but dye may leak into unintended regions causing incorrect marking
Solution Approach 1:
The patent segments the detection task into two independent parts: detecting cell positions using the brightfield channel (which is not affected by dye leakage) and detecting marker signal using the fluorescence channel. The one-class classifier then integrates these segmented detections, allowing it to distinguish between true positive marker signals and false positives from stain bleeding.
Solution Approach 2:
The patent uses the brightfield channel as a copy or reference of the cell positions that is independent of the fluorescence channel. This reference copy allows the system to verify which cells should have received the marker, enabling it to distinguish between cells that should be fluorescent and those that show fluorescence only due to stain bleeding.
3Object-generated harmful factors
If conventional image processing is used to filter oversized objects, then stain bleeding is reduced, but transfection rate calculation becomes inaccurate
Solution Approach 1:
The patent implements a feedback mechanism where the one-class classifier continuously refines its detection by comparing results from both channels. The system uses the brightfield channel information to correct and refine the fluorescence channel detection, providing feedback that improves both stain bleeding filtering and transfection rate calculation accuracy simultaneously.
Solution Approach 2:
The patent changes the detection parameters by using a one-class classifier that operates on combined channel information rather than relying on fixed threshold values. This parameter change allows the system to adapt to varying conditions and accurately calculate transfection rates while effectively filtering stain bleeding.
4Measurement precision
If manual annotation is performed to create training data, then model training quality is improved, but time consumption increases
Solution Approach 1:
The patent implements self-service by using the one-class classifier to automatically generate training annotations from the image data itself. The system leverages the brightfield channel and fluorescence channel information to automatically identify cell positions and create training data, eliminating the need for manual annotation while maintaining high data quality.
Solution Approach 2:
The patent creates automated annotations by copying and transforming the detection results from the brightfield channel and fluorescence channel. The one-class classifier processes these channel data to generate training annotations, effectively copying the detection logic into an automated system that produces high-quality training data without manual intervention.
Data Source
AI summary
A method, a device, and a computer program product captures microscopy objects in image data that includes first images recorded with a first contrast and second images recorded with a second contrast, wherein in each case, one of the first and one of the second images can be correspondingly assigned to each other. The method includes capturing information indicating microscopy objects in at least one of the second images, transferring the captured information to those of the first images which correspond to the at least one of the second images, and capturing information indicating microscopy objects in the first images, to which the captured information of the second images was transferred by using the transferred information.