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A deep learning-based stripe self-correction method for stitched microscopic images.

Shu Wang^1,2,3, Xiaoxiang Liu², Yueying Li¹

¹College of Mechanical Engineering and Automation, Fuzhou University, Fuzhou, 350108, China.

Nature Communications

|September 5, 2023

Summary

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This summary is machine-generated.

Stripe artifacts in microscope images degrade quality. A new deep learning method, SSCOR, uses self-correction to remove these stripes and other artifacts, improving image analysis.

Area of Science:

Microscopy
Image Processing
Artificial Intelligence

Background:

Stitched fluorescence microscope images often contain stripe artifacts.
These artifacts negatively impact image quality and quantitative analysis.

Purpose of the Study:

To introduce a deep learning-based method, SSCOR, for correcting stripe artifacts in microscope images.
To develop a method that can adaptively correct various artifacts without manual intervention.

Main Methods:

Proposed a proximity sampling scheme and adversarial reciprocal self-training paradigm.
SSCOR utilizes stripe-free image patches to correct adjacent artifact-containing patches.
The method operates without requiring physical parameter estimation or manual annotations.

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Main Results:

SSCOR effectively corrects non-uniform, oblique, and grid stripes.
The method also removes scanning, bubble, and out-of-focus artifacts.
Achieved state-of-the-art performance across diverse imaging conditions and modalities.

Conclusions:

SSCOR offers an intelligent, prior-free solution for microscope image restoration.
This method enhances image quality for more precise biomedical applications.
Provides a valuable tool for researchers and microscope manufacturers.