Biological Image Transformation Using Wavelet GAN Enhancement
Find Innovative SolutionsGenerate Solutions
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 details are improved, but equipment complexity, cost, 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 virtual copy of the fluorescence modality without requiring actual fluorescence microscopy equipment. This copying approach provides rich visual information while avoiding the complexity of fluorescence imaging equipment
Solution Approach 2:
The patent introduces a machine learning model as an intermediary between bright-field imaging and fluorescence image analysis. This intermediary transforms simple bright-field images into enhanced representations that provide fluorescence-like visual information without direct fluorescence imaging
2Loss of information
If bright-field images are transformed into enhanced images, then visual details are improved, but transformation difficulty increases due to class imbalance and geometry preservation requirements
Solution Approach 1:
The patent transforms images by changing parameters in the frequency domain using wavelet coefficients. By operating in this transformed domain and applying learned transformations, the model can enhance visual details while maintaining geometric integrity, overcoming the difficulties of direct spatial domain transformation
Solution Approach 2:
The patent employs a generative adversarial network with a discriminator that provides feedback to the generator. This feedback mechanism guides the transformation process to preserve geometric relationships while enhancing visual details, solving the class imbalance and geometry preservation challenges
3Reliability
If fluorescence imaging is used to obtain rich visual information, then downstream analysis quality is improved, but processing time and resource consumption increase
Solution Approach 1:
The patent performs preliminary enhancement of bright-field images using a trained machine learning model before downstream analysis. This preliminary action creates enhanced images that are ready for immediate analysis without requiring time-consuming fluorescence imaging procedures, thus improving both quality and productivity
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.