Transfection Imaging for Cell Wall Annotation Accuracy
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Solution Overview
Problem
The manual creation of annotations for instance segmentation of cells in microscope images is time-consuming, error-prone, and costly, and often results in inconsistencies due to the need for expert knowledge and effort.
Innovation Solution
A computer-implemented method trains machine-learning algorithms using cell wall annotations as ground truth, which can be created manually or automatically through transfection imaging, allowing for the localization of cell walls and subsequent use in various image processing tasks such as instance segmentation and cell counting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual creation of annotations is performed, then accuracy of annotations is improved, but time consumption and cost increase significantly
Solution Approach 1:
The patent applies preliminary action by using transfection imaging to pre-identify and mark cell boundaries before the main annotation process. The transfection image shows fluorescently labeled cell membranes, which automatically provide preliminary boundary information that guides subsequent annotation steps, reducing the time required for manual delineation while maintaining accuracy.
Solution Approach 2:
The patent uses transfection imaging as an intermediary between raw microscope images and final annotations. The transfection image serves as a mediator that highlights cell boundaries through fluorescent labeling, making it easier for annotators to accurately trace cell contours without having to interpret complex overlapping cell structures directly from the original image.
2Measurement precision
If manual creation of annotations is performed, then annotation quality is improved, but expertise requirement and cost increase
Solution Approach 1:
The transfection image acts as an intermediary that simplifies the annotation task by pre-highlighting cell boundaries through fluorescent markers. This reduces the expertise required because the difficult task of identifying cell boundaries in complex overlapping images is transformed into a simpler task of tracing pre-marked boundaries in the transfection image.
Solution Approach 2:
The transfection imaging performs preliminary identification of cell structures before annotation begins. By pre-labeling cell membranes with fluorescent markers, the system prepares the data in advance, reducing the skill level needed for the actual annotation process while maintaining high annotation quality.
3Productivity
If transfection imaging with automated processing is used, then annotation efficiency is improved, but automation extent increases
Solution Approach 1:
The system applies self-service by using the transfection image's inherent fluorescent signals to automatically generate boundary information without requiring extensive manual intervention. The automated processing extracts cell contours from the fluorescent patterns in the transfection image, allowing the data to annotate itself with minimal human input, thereby提高效率 while maintaining reasonable automation levels.
Data Source
AI summary
Various aspects of the disclosure relate to techniques for recognizing cell structures in microscope images. In particular, techniques for recognizing cell walls, i.e. cell edges, in microscope images, e.g. phase contrast images, are described. For this purpose, a machine-learned algorithm, e.g. an artificial neural network, can be used. Techniques of how annotations can be created as a ground truth for the training of the machine-learned algorithm, e.g. based on the fluorescence channel of transfection image data, are described. Further actions can then be performed based on correspondingly localized cell walls, e.g. cell-instance annotations that segment cell instances, for the training of a further machine-learned algorithm.