Mueller Matrix Microscopy Neural Network for Cervical Tissue Analysis
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Solution Overview
Problem
There is a lack of clinical tools for early and accurate detection of spontaneous preterm birth risk due to insufficient understanding of the molecular events driving preterm birth, particularly in cervical remodeling processes.
Innovation Solution
The use of Mueller matrix microscopy combined with machine learning approaches, specifically training neural networks with second harmonic generation (SHG) and two-photon excitation fluorescence (TPEF) microscopy data, to visualize and determine the amount of collagen and elastin in cervical tissue, enabling accurate classification and visualization of these components.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional imaging methods are used to detect collagen and elastin in cervical tissue, then the detection process is simple, but the measurement precision and ability to distinguish between different tissue components is insufficient
Solution Approach 1:
The patent combines Mueller matrix polarimetry microscopy with deep learning algorithms to create an integrated system that achieves high-precision detection of collagen and elastin. The Mueller matrix data provides comprehensive optical properties while the neural network classifier accurately distinguishes between different tissue components, resolving the technical contradiction between measurement precision and device complexity.
Solution Approach 2:
The patent introduces a deep learning classifier as an intermediary between the Mueller matrix imaging system and the final tissue characterization. This intermediary processes the complex Mueller matrix data and translates it into accurate collagen and elastin detection, enabling high measurement precision while managing the inherent complexity of the imaging system.
2Measurement precision
If advanced microscopy techniques like SHG and TPEF are used to obtain ground truth data, then the measurement precision improves, but the device complexity and cost increase
Solution Approach 1:
The patent uses SHG and TPEF microscopy to obtain ground truth data for training the neural network classifier. These advanced techniques provide excessive detail and precision for the specific task of training the classifier, enabling high measurement precision in the final application while the trained classifier can then process simpler Mueller matrix data for routine detection.
3Measurement precision
If machine learning approaches are implemented to classify tissue components, then the detection accuracy improves, but the difficulty of detecting and measuring increases due to the need for training data and model development
Solution Approach 1:
The patent performs preliminary action by training the deep learning classifier offline using ground truth data from SHG and TPEF microscopy. This pre-training process prepares the model for accurate tissue component classification, and the trained classifier can then be applied to new Mueller matrix data without requiring real-time complex processing, thus improving detection accuracy while managing implementation difficulty.
Solution Approach 2:
The patent creates a computational model (neural network classifier) that learns from ground truth data and reproduces the ability to distinguish collagen and elastin. This copied knowledge allows the system to achieve high classification accuracy without requiring the complex hardware of SHG/TPEF microscopy for every measurement, reducing the ongoing difficulty of detection while maintaining precision.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This method provides a novel and effective way to characterize collagen and elastin fibers in cervical tissue, potentially leading to improved detection of preterm birth risk and development of clinical tools for assessing cervical function.
Implementation Method 1
The SHG can be used to extract the ground truth for collagen
Implementation Method 2
the TPEF can be used to extract the ground truth for elastin
Implementation Method 3
Mueller matrix polarimetry microscopy data
Data Source
AI summary
Systems and methods for visualizing, and/or determining the amount of, collagen and elastin in tissue are provided. Training data can be generated using Mueller matrix polarimetry microscopy data, combined with second harmonic generation (SHG) and/or two photon excitation fluorescence (TPEF) microscopy data as ground truth. The SHG and/or TPEF data can be used to train a neural network for feature extraction, and classification can be performed. The components and decompositions of the Mueller matrix data can be arranged as individual channels of information, forming one voxel per sample.