Contextual Mask Generation for Non-Destructive Biological Sample Characterization
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Solution Overview
Problem
Current microscopy techniques face challenges in obtaining fluorescence imaging data without harming live cell samples and in efficiently identifying and characterizing biological structures within numerous images, relying heavily on human expertise and intuition.
Innovation Solution
The development of a system that generates contextual masks from quantitative image data using neural networks, allowing for the labeling of biological structures and providing quantitative parameters without the need for destructive processes or expert input, thereby enhancing the characterization and analysis of biological samples.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If fluorescence imaging is used to obtain contextual data, then information completeness is improved, but sample integrity deteriorates due to harmful effects on live cells
Solution Approach 1:
The patent creates a computational copy of the contextual information that would be obtained from fluorescence imaging. A neural network is trained on paired quantitative phase and fluorescence images to learn the mapping between phase imaging data and fluorescence characteristics. Once trained, the network can generate synthetic fluorescence-like contextual masks from quantitative phase images alone, providing the necessary contextual information without requiring actual fluorescence imaging of live samples.
2Measurement precision
If expert annotation is used to label biological structures, then measurement precision is improved, but productivity deteriorates due to time-consuming manual processes
Solution Approach 1:
The patent implements self-service by enabling the system to automatically generate contextual masks and identify biological structures without requiring expert annotation. The neural network, once trained on a limited set of expert-annotated images, autonomously processes subsequent images to produce accurate structural labels. This transfers the intelligence to the machine, allowing it to serve itself in identifying and characterizing biological structures across large image datasets.
Solution Approach 2:
The patent applies preliminary action by training the neural network in advance on a curated dataset of expert-annotated images. This preliminary training phase captures expert knowledge and patterns, which are then stored within the network's weights and biases. During actual processing, the pre-trained network rapidly applies this learned knowledge to new images without requiring real-time expert involvement, thus achieving both high accuracy and productivity.
3Object-affected harmful factors
If quantitative phase imaging is used instead of fluorescence imaging, then sample integrity is improved, but information completeness deteriorates due to lack of contextual data
Solution Approach 1:
The patent introduces a computational intermediary—the neural network—that translates quantitative phase imaging data into contextual information typically obtained from fluorescence imaging. The network acts as a mediator, learning the relationship between phase imaging characteristics and fluorescence-based contextual markers, then using this learned mapping to generate synthetic contextual masks. This intermediary enables the system to derive contextual specificity from non-destructive phase imaging data.
Data Source
AI summary
A system generates a context mask based on quantitative image data. The system obtains the quantitative image data which was captured via a quantitative imaging of a sample. The quantitative image data is compared to previous quantitative image data through application of the quantitative image data to a neural network trained using the previous quantitative image data and corresponding constructed context masks. The comparison generates the context mask for the quantitative image data. The context mask provides context for the quantitative parameter values that facilities characterization of the sample.