Generative Model for Co-Registered Microscope Images
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Solution Overview
Problem
The generation of registered training data for machine learning models in microscopy is hindered by the need for manual effort to create co-registered image pairs, which is time-consuming and resource-intensive.
Innovation Solution
A computer-implemented method and microscopy system that use a generative model to create co-registered microscope image pairs from unregistered image datasets with minimal manual effort by identifying object feature variables and imaging-property feature variables, and generating images that match in object positions but differ in imaging properties.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual annotation is used to create registered image pairs, then training data quality is improved, but time consumption and resource investment increase significantly
Solution Approach 1:
The patent uses CycleGAN to generate synthetic registered image pairs by copying and transforming structures from unregistered images. The model learns to translate between different image types (e.g., brightfield to fluorescence) while maintaining structural consistency, creating training data without manual annotation.
Solution Approach 2:
The system performs self-annotation by using the unregistered images themselves as input to generate the corresponding registered images. The CycleGAN model automatically identifies and replicates structural features, eliminating the need for external expert annotation while maintaining training data quality.
2Productivity
If unregistered image datasets are used, then data collection is simplified and time-consuming registration is avoided, but the images cannot be used for training models requiring co-registered pairs
Solution Approach 1:
The patent transforms the parameter space of images by learning a mapping between different image representations. CycleGAN modifies the structural and textural parameters of unregistered images to generate synthetic registered pairs, enabling compatibility with training requirements while maintaining collection efficiency.
Solution Approach 2:
The CycleGAN model acts as an intermediary that bridges unregistered images and the requirements for registered training data. It generates intermediate synthetic images that satisfy the co-registration constraint without requiring actual registration of the original datasets.
3Extent of automation
If CycleGAN is used to generate images without explicit registration, then manual effort is reduced, but positional correspondence between objects in input and output images is not guaranteed
Solution Approach 1:
The patent incorporates feedback mechanisms where the generated images are evaluated for structural consistency with the input images. The loss function includes terms that enforce structural similarity, providing feedback to the generator to maintain positional correspondence between objects across different image types.
Data Source
AI summary
A computer-implemented method for generating pairs of registered microscope images includes training a generative model to create generated microscope images from input feature vectors comprising feature variables. The training uses image data sets which respectively contain microscope images of microscopic objects but which differ in an imaging/image property. It is identified which of the feature variables are object feature variables, which define at least object positions of microscopic objects in generated microscope images, and which of the feature variables are imaging-property feature variables, which determine a depiction of the microscopic objects in generated microscope images depending on the imaging/image property. At least a pair of generated microscope images is created from feature vectors with corresponding object feature variables and differing imaging-property feature variables, so that the generated microscope images show objects with corresponding object positions, but with a difference in the imaging/image property.