Fluorescence Microscope Image Processing for Crosstalk Removal
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Solution Overview
Problem
Conventional fluorescence microscopes face challenges in accurately imaging with high accuracy due to fluorescence crosstalk, which is difficult to remove without acquiring fluorescence spectra that vary based on experimental conditions.
Innovation Solution
An information processing apparatus that constructs a learning model using paired images with and without fluorescence crosstalk, allowing it to generate images with reduced crosstalk without relying on fluorescence spectra variations, by employing a controller to acquire and process images with machine learning techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If fluorescence spectra are acquired to remove fluorescence crosstalk, then image accuracy is improved, but experimental complexity and time consumption increase
Solution Approach 1:
The patent creates a synthetic image dataset that copies and transforms real fluorescence images through deep learning models. The model learns to remove fluorescence crosstalk by training on synthesized image pairs where the same sample is represented both with and without crosstalk effects, allowing accurate crosstalk removal without acquiring actual fluorescence spectra data
Solution Approach 2:
The patent replaces the traditional optical/mechanical approach of using multiple filters and spectrometers to separate fluorescence wavelengths with a computational approach. Instead of physically separating spectra, the system uses neural networks to computationally remove crosstalk from images, substituting complex optical systems with software-based processing
2Productivity
If multiple wavelengths are imaged simultaneously to improve imaging speed, then productivity is improved, but fluorescence crosstalk increases
Solution Approach 1:
The patent converts the harmful fluorescence crosstalk that occurs during simultaneous multi-wavelength imaging into a beneficial training signal. By intentionally creating image datasets that contain crosstalk and pairing them with ground truth images without crosstalk, the system learns to recognize and remove the harmful effects, turning the problem into a learning opportunity
Solution Approach 2:
The patent introduces a deep learning model as an intermediary between the simultaneous multi-wavelength imaging process and the final clean image output. The model acts as a mediator that processes the raw images containing crosstalk and transforms them into corrected images, allowing simultaneous imaging to proceed while eliminating the harmful crosstalk effects computationally
Data Source
AI summary
An information processing apparatus 10 according to the present disclosure includes a controller 11 configured to acquire, based on learning data that associates third images, which are acquired by a fluorescence microscope 1 and has no fluorescence crosstalk, with fourth images, which are acquired by the fluorescence microscope 1 and has fluorescence crosstalk, a learning model constructed by learning the third images corresponding to the fourth images, and generate, based on the acquired learning model, second images with reduced fluorescence crosstalk in first images of a sample S, which are acquired by the fluorescence microscope 1.