Biological image transformation using machine-learning models
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Solution Overview
Problem
Bright-field images of biological samples lack rich visual details due to inherent class imbalance and are difficult to transform into enhanced, high-quality images suitable for downstream analyses, while fluorescence images are resource-intensive and challenging to obtain at scale.
Innovation Solution
A machine-learning model is trained to generate enhanced images by transforming bright-field images into fluorescence-like images using wavelet coefficients, utilizing a generator and discriminator, such as a conditional GAN model, to enhance image quality and maintain geometric integrity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If fluorescence images are obtained to provide rich visual information, then image quality and visual detail are improved, but equipment complexity and resource requirements increase
Solution Approach 1:
The patent uses a machine learning model to generate synthetic fluorescence images from bright-field images, creating a computational copy of the desired image modality without requiring physical fluorescence microscopy equipment. This copying approach allows obtaining fluorescence-like visual information while avoiding the complexity of fluorescence imaging equipment and materials.
2Loss of information
If fluorescence imaging is performed to obtain high-quality images, then image quality is improved, but time consumption and processing resources increase
Solution Approach 1:
The patent replaces the mechanical and chemical processes of fluorescence imaging (staining, excitation, detection) with a computational machine learning model. This substitution transforms a time-consuming wet lab process into a rapid computational operation that generates high-quality fluorescence-like images without physical staining or specialized equipment operation.
3Ease of manufacture
If bright-field images are used to reduce resource requirements, then cost and resource usage are reduced, but image quality and visual detail are lost
Solution Approach 1:
The patent introduces a machine learning model as an intermediary that bridges bright-field and fluorescence image modalities. The model takes simple, low-cost bright-field images as input and transforms them into detailed fluorescence-like images, allowing the system to benefit from both the cost efficiency of bright-field imaging and the visual richness of fluorescence imaging.
4Loss of information
If image transformation is performed to enhance bright-field images, then visual detail is improved, but maintaining geometric integrity becomes more difficult
Solution Approach 1:
The patent employs a generative adversarial network where a discriminator component provides feedback to the generator, evaluating whether the transformed image maintains geometric fidelity while gaining visual detail. This feedback mechanism ensures that the transformation process preserves the underlying geometric structure of the bright-field image while enhancing visual information, solving the contradiction between detail enhancement and geometric integrity.
Data Source
AI summary
Described are systems and methods for training a machine-learning model to generate image of biological samples, and systems and methods for generating enhanced images of biological samples. The method for training a machine-learning model to generate images of biological samples may include obtaining a plurality of training images comprising a training image of a first type, and a training image of a second type. The method may also include generating, based on the training image of the first type, a plurality of wavelet coefficients using the machine-learning model; generating, based on the plurality of wavelet coefficients, a synthetic image of the second type; comparing the synthetic image of the second type with the training image of the second type; and updating the machine-learning model based on the comparison.