Automatic Fluorescence Microscopy Acquisition for Consistent SNR
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Solution Overview
Problem
Existing microscopy workflows face challenges in efficiently acquiring large sets of images with consistent signal-to-noise ratios across heterogeneous samples, requiring tedious manual adjustments of acquisition settings and leading to suboptimal results.
Innovation Solution
A computer-implemented method that automatically adjusts illumination settings for each region of interest to meet a target signal-to-noise ratio, enabling efficient acquisition of microscopy images for machine-learning applications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual adjustment of acquisition settings is performed for each imaging position, then image quality can be optimized for specific regions, but the acquisition process becomes tedious and time-consuming
Solution Approach 1:
The system performs self-adjustment of illumination settings by automatically analyzing image quality metrics and modifying acquisition parameters without user intervention. The microscope system acquires test images, evaluates their quality, and autonomously optimizes illumination settings for each region of interest, eliminating the need for manual adjustment while maintaining high image quality
Solution Approach 2:
The system implements a feedback loop where acquired images are analyzed to evaluate quality metrics, and this information is used to automatically adjust illumination settings for subsequent acquisitions. The system continuously monitors image quality and refines acquisition parameters based on the evaluated feedback, enabling automated optimization across heterogeneous samples
2Productivity
If a single set of acquisition parameters is used for all regions, then the acquisition process is simplified and fast, but image quality becomes suboptimal with some images too bright or too dark
Solution Approach 1:
The system applies different illumination settings to different regions of interest based on their specific characteristics. Instead of using uniform acquisition parameters across the entire sample, the system analyzes each region's properties and customizes illumination parameters locally to achieve optimal image quality for heterogeneous samples with varying staining intensities and fluorophore distributions
Solution Approach 2:
The system dynamically adjusts acquisition parameters based on real-time evaluation of image quality. Rather than using static, pre-defined settings, the illumination parameters are adaptively modified during the acquisition process based on the actual sample characteristics and observed image quality metrics, enabling both speed and quality optimization
3Reliability
If excessive illumination is used to ensure visible fluorophores in all regions, then fluorophores become visible, but photobleaching and phototoxicity increase
Solution Approach 1:
The system optimizes illumination parameters such as intensity, exposure time, and laser power to achieve the minimum necessary illumination for reliable fluorophore detection. By precisely controlling and adjusting these parameters based on actual sample characteristics rather than using excessive illumination, the system ensures fluorophore visibility while minimizing photobleaching and phototoxicity effects
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
Enables efficient and consistent image acquisition with minimal user intervention, reducing photobleaching and phototoxicity while ensuring high-quality datasets for training and testing machine-learning models.
Implementation Method 1
acquire, for each region of interest of the plurality of regions of interest, at least one microscopy image
Data Source
AI summary
A computer-implemented image acquisition method includes receiving a user input indicating at least one quality condition. The quality condition includes a target signal-to-noise ratio associated with a plurality of regions of interest of a sample arrangement to be imaged using a fluorescence microscope. The method further includes causing the fluorescence microscope to automatically acquire, for each region of interest of the plurality of regions of interest, at least one microscopy image using illumination settings automatically determined such that the target signal-to-noise ratio is met, and generating a dataset for generating, training, validating and/or testing a machine-learning model. The dataset includes the acquired microscopy images or references thereto.