CNN Microscopy Image Reconstruction for Fast Phase Contrast
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for capturing phase contrast microscopy images are slow, expensive, and difficult to implement, and imaging devices capable of phase contrast imaging are more costly than those for brightfield imaging.
Innovation Solution
A system and method using a convolutional neural network, specifically a U-net convolutional neural network, is employed to convert brightfield microscopy images to phase contrast images by extracting phase information and reconstructing interference patterns, utilizing a training dataset to update network parameters and minimize pixel-wise sum of squared differences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional phase contrast microscopy hardware and software are used, then phase contrast images can be captured, but the method is slow, expensive and difficult to implement
Solution Approach 1:
The patent creates a computational copy of phase contrast imaging capability through a neural network model trained on paired brightfield and phase contrast images. Instead of requiring actual phase contrast hardware, the system learns to generate phase contrast-like images from brightfield inputs, effectively copying the functional output without the complex hardware infrastructure
Solution Approach 2:
The patent replaces the mechanical and optical complexity of phase contrast microscopy hardware with a software-based neural network system. The physical optical path modifications needed for phase contrast are substituted by computational processing that learns the transformation from brightfield to phase contrast appearance through training data
2Measurement precision
If traditional phase contrast microscopy methods are used, then phase contrast images can be captured, but the acquisition time is long
Solution Approach 1:
The neural network is pre-trained on a large dataset of paired brightfield and phase contrast images before deployment. This preliminary training phase allows the model to learn the complex transformation relationships in advance, so that during actual use, phase contrast images can be generated instantly from brightfield inputs without requiring time-consuming multi-step acquisition procedures
Solution Approach 2:
The system creates a computational model that copies the appearance and information content of phase contrast images through neural network processing, enabling rapid generation of phase contrast-like images from standard brightfield microscopy without requiring the time-intensive Z-stack acquisition and processing of traditional methods
3Measurement precision
If phase contrast microscopy hardware is used, then phase contrast images can be captured, but the cost is high
Solution Approach 1:
The patent creates a software-based copy of phase contrast imaging capability that runs on standard computing hardware. Instead of requiring expensive specialized optical components and hardware modifications, the system uses a trained neural network model that can be deployed on conventional computers or servers, dramatically reducing the cost barrier while maintaining image quality
Solution Approach 2:
The patent substitutes expensive mechanical and optical hardware with affordable software processing. The complex optical path modifications, specialized lenses, and hardware components required for traditional phase contrast microscopy are replaced by a neural network algorithm that runs on standard computing infrastructure, making the technology accessible and cost-effective
Data Source
Figure 1
Figure 2
Figure 3
AI summary
The present invention particularly relates to a system and method using a convolutional neural network such as a U-net convolutional neural network to reconstruct microscopy images, for example phase contrast microscopy images. The method as per the present invention comprises the steps of obtaining a training data set comprising of a plurality of brightfield microscopy images and a plurality of phase contrast microscopy images, training a neural network based on the data set to extract phase information in the form of an interference pattern from each of the plurality of brightfield microscopy images and to further use the trained neural network to reconstruct phase contrast microscopy images from the brightfield microscopy images. The system as per the present invention comprises a memory means, a processing means, a storage and retrieval means, and a display means. The memory means stores the data set, and the processing means trains the neural network based on the data set and further enables reconstruction of phase contrast images.