Machine Learning Model for Microscope Image Optical Sectioning
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Solution Overview
Problem
Existing microscopy techniques require extensive data sets and often necessitate additional hardware or sample overloading to achieve high-quality optical sectioning and image processing, which can lead to sample damage and increased costs.
Innovation Solution
A method for training a machine learning system with a processing model using a virtual processing mapping, where a fine stack is recorded to determine a target microscope image, and a coarse stack is used to train the model, allowing for the generation of high-quality microscope images without additional equipment or sample overloading.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If classical deconvolution mapping is used to generate optically sharp sectioning, then image quality is improved, but sample loading is considerably increased due to recording a stack of multiple microscope images
Solution Approach 1:
The patent applies partial action by recording only a single microscope image instead of a complete stack of multiple images. The machine learning model is trained to generate optically sharp sectioning from this single partial image, thereby achieving the desired image quality while significantly reducing sample loading and exposure time.
Solution Approach 2:
The patent uses a machine learning model to create a virtual copy of the optical sectioning process. Instead of physically capturing multiple images through complex optical arrangements, the trained model generates synthetic optically sharp images from single wide-field images, eliminating the need for extensive sample exposure.
2Measurement precision
If structured illumination or laser scanning microscopy is used to achieve optical sectioning, then image quality is improved, but device complexity and acquisition costs are increased
Solution Approach 1:
The patent replaces complex mechanical and optical systems (structured illumination apparatus, laser scanning systems, spinning aperture disks) with a computational approach. A machine learning model trained on paired data performs optical sectioning through software processing of wide-field images, eliminating the need for specialized hardware while achieving comparable or superior results.
Solution Approach 2:
The patent creates a universal processing model that can perform multiple functions: optical sectioning, super-resolution enhancement, denoising, and spectral demixing. This single machine learning model replaces multiple specialized microscopy systems, allowing one wide-field microscope to achieve capabilities previously requiring complex dedicated equipment.
3Reliability
If a large number of images are recorded and processed to train neural networks, then model accuracy is improved, but training time and computational resources are increased
Solution Approach 1:
The patent performs preliminary action by pre-training the machine learning model using publicly available image datasets (such as ImageNet) before fine-tuning with microscopy-specific data. This pre-training establishes a strong foundation that reduces the amount of domain-specific training data and time required to achieve high accuracy, thereby reducing overall training time while maintaining model reliability.
Data Source
AI summary
A method for training a machine learning system having a processing model for a sample type, which processes microscope images of samples of the sample type by virtual processing mapping, comprising recording at least one fine stack of a sample of the sample type, wherein the at least one fine stack comprises microscope images of the sample registered with respect to one another, determining at least one target microscope image based on the fine stack and the virtual processing mapping, creating an annotated data set comprising at least the target microscope image and a learning microscope image, wherein the learning microscope image is based on a coarse stack capturing the sample coarser than the fine stack, optimizing the processing model on the basis of the annotated data set.