Synthetic Microscopy Image Generation for Sequencing Intensity Extraction
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Solution Overview
Problem
Existing sequencing technologies face challenges in accurately extracting intensity values from microscopy images due to low resolution and high spot density, leading to inaccuracies in base-calling and sequencing results.
Innovation Solution
The use of machine learning models trained on synthetic microscopy images generated through advanced imaging techniques or simulations of sequencing biochemistry to produce high-resolution images, allowing for precise intensity extraction and base-calling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional imaging techniques are used to capture microscopy images of nucleic acid sequences, then the imaging process is simple and inexpensive, but the resolution is low and spot density is high, resulting in inaccurate intensity value extractions
Solution Approach 1:
The patent creates synthetic microscopy images that replicate the appearance and characteristics of real microscopy images but with known ground truth intensity values. These synthetic copies are generated through simulation of the imaging process, allowing the system to learn accurate intensity extraction without requiring complex hardware modifications.
Solution Approach 2:
The patent performs preliminary generation of synthetic training images with known intensity values before the actual intensity extraction task. By pre-computing synthetic images with accurate ground truth labels, the system prepares training data in advance that enables the machine learning model to learn accurate intensity extraction patterns.
2Productivity
If microscopy images with high spot density are used, then more nucleic acid sequences can be analyzed simultaneously, but the resolution decreases and intensity value extraction becomes inaccurate
Solution Approach 1:
The patent generates synthetic images that mimic high spot density scenarios while maintaining accurate ground truth intensity information. By creating virtual copies of high-density imaging scenarios with known correct values, the system trains models to accurately extract intensities even in high-density conditions.
Solution Approach 2:
The patent replaces physical imaging system improvements with a computational approach. Instead of upgrading hardware to achieve better resolution at high spot densities, the system uses machine learning models trained on synthetic data to computationally correct and accurately extract intensity values from high-density images.
3Measurement precision
If advanced imaging techniques or simulations are used to generate high-resolution synthetic images, then intensity extraction precision improves, but the device complexity and computational requirements increase
Solution Approach 1:
The patent creates computational copies of the imaging process through simulation. By modeling the physics and chemistry of the sequencing imaging process, the system generates synthetic images that capture the essential characteristics without requiring actual advanced hardware, thus achieving high precision through computation rather than complex equipment.
Solution Approach 2:
The patent introduces synthetic images as an intermediary between the imaging process and intensity extraction. These synthetic images serve as a bridge that connects the complex imaging process with the intensity extraction algorithm, providing training data with known ground truth values that enable accurate extraction without direct complex hardware intervention.
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
The approach enables accurate determination of nucleic acid sequences by generating high-resolution synthetic images that mimic real images, improving the precision of intensity extraction and base-calling in sequencing processes.
Implementation Method 1
Each base emits light at a characteristic wavelength due to its specific fluorescent label
Data Source
AI summary
Systems and methods for synthetic image generation and machine learning analysis for biological substances. In one example, a set of real microscopy images representing objects of a biological substance is received. Each object corresponds to one or more pixels of the real microscopy image. Features of the real microscopy image and the objects are accessed and a set of synthetic microscopy images representing the objects of the biological substance is generated based on the features. In another example, a set of synthetic microscopy images is generated using seed intensities and features extracted or known from a set of real microscopy images. A set of seed images for the synthetic microscopy images is generated from the seed intensities. A trained machine learning model is generated to generate intensity values for additional real microscopy images using the synthetic microscopy images and the seed images as training data.