Biological Image Transformation Using Wavelet GAN Enhancement
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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 used to obtain rich visual information, then image quality and visual details 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 allows obtaining fluorescence-like visual information while avoiding the complexity of fluorescence imaging hardware
Solution Approach 2:
The patent replaces the mechanical/optical fluorescence imaging system with a computational approach using machine learning models. Instead of using physical fluorescence microscopy equipment to capture images, the system uses trained neural networks to synthesize fluorescence images from bright-field inputs, substituting a computational process for a physical imaging system
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 extraction
Solution Approach 1:
The patent transforms bright-field images into enhanced images by changing key parameters through machine learning. The model learns to modify image characteristics such as contrast, intensity distribution, and frequency content to generate fluorescence-like images from bright-field inputs, effectively changing the parametric representation of the image data
Solution Approach 2:
The patent segments the image transformation process into distinct functional components within the neural network architecture. The generator model processes different frequency components and image features separately, transforming bright-field images through multiple computational stages that handle different aspects of the enhancement task independently
3Loss of information
If fluorescence images are obtained through traditional methods, then rich visual information is achieved, but time consumption and processing resources increase
Solution Approach 1:
The patent performs preliminary action by pre-training machine learning models on large datasets of paired bright-field and fluorescence images. This offline training phase prepares the model in advance so that during actual use, fluorescence images can be generated instantly from bright-field inputs without requiring time-consuming physical fluorescence imaging procedures
Solution Approach 2:
The patent replaces the time-consuming physical fluorescence imaging process with a rapid computational synthesis approach. The trained machine learning model can generate fluorescence-like images from bright-field inputs in seconds, substituting a fast computational process for a slow physical imaging process and significantly improving 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.