Microscopy Object Detection Across Contrasts for Unmarked Cells
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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 and device that utilize image recognition and machine learning to identify non-transfected cells by comparing images with different contrasts, using a one-class classifier to automatically locate and capture microscopy objects in images with low-pass filtering, threshold operations, and contour finding, and transfer this information between contrasts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If transfection with markers or dyes is used to improve cell visibility, then optical identifiability of cells is improved, but some cells are missed (non-transfected) and cannot be detected
Solution Approach 1:
The method segments the detection task into two independent pathways: (1) fluorescence-based detection for transfected cells, and (2) machine learning-based detection for non-transfected cells using brightfield images. This segmentation allows each pathway to optimize for its specific detection target, resolving the contradiction between visibility improvement and detection completeness.
Solution Approach 2:
A machine learning model acts as an intermediary that bridges the gap between brightfield images and cell detection. The model is trained to recognize cell morphologies in brightfield images, enabling detection of non-transfected cells that would otherwise be invisible, thus completing the detection chain.
2Measurement precision
If dye staining is used to mark cells, then visibility of marked regions is improved, but dye leaks into unintended regions causing false positives
Solution Approach 1:
The machine learning model serves as an intermediary that interprets brightfield image features to identify cells, replacing the direct reliance on fluorescence staining. This eliminates the harmful effect of dye leakage while maintaining detection accuracy through learned morphology recognition.
Solution Approach 2:
The method applies different detection strategies to different cell populations: fluorescence-based detection for transfected cells and ML-based brightfield detection for non-transfected cells. This local quality approach ensures each cell type is detected by the most appropriate method, avoiding false positives from stain bleeding.
3Reliability
If filtering based on application-specific values is used to remove oversized objects, then false positives from stain bleeding are reduced, but the complexity of the processing pipeline increases
Solution Approach 1:
The machine learning model performs preliminary action by pre-identifying all potential cell locations in the brightfield image before any filtering or fluorescence matching occurs. This preliminary detection includes both transfected and non-transfected cells, eliminating the need for subsequent complex filtering to remove false positives.
Solution Approach 2:
Instead of using the flawed fluorescence signal as the primary detection mechanism, the method creates a parallel copy of the detection process using brightfield images and machine learning. This copy independently identifies cells without being affected by stain bleeding, simplifying the overall pipeline.
4Measurement precision
If conventional transfection methods are used, then marked cells are visible, but non-transfected cells remain invisible leading to incomplete data sets
Solution Approach 1:
The detection system is segmented into two independent detection streams: one for fluorescently marked cells and one for unmarked cells using brightfield imaging and ML. This ensures that information about both transfected and non-transfected cells is captured, eliminating the information loss inherent in conventional single-modality approaches.
Solution Approach 2:
The method uses excessive action by applying machine learning-based detection to all cells regardless of transfection status, rather than relying solely on fluorescence. This partial redundancy ensures that even cells that fail to take up the marker are still detected through their morphological features in the brightfield channel.
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.