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

Shu Wang, Xiaoxiang Liu, Yueying Li, Xinquan Sun, Qi Li, Yinhua She, Yixuan Xu, Xingxin Huang, Ruolan Lin, Deyong Kang, Xingfu Wang, Haohua Tu, Wenxi Liu (), Feng Huang () and Jianxin Chen ()
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Shu Wang: Fuzhou University
Xiaoxiang Liu: Fuzhou University
Yueying Li: Fuzhou University
Xinquan Sun: Fuzhou University
Qi Li: Fuzhou University
Yinhua She: Fuzhou University
Yixuan Xu: Fuzhou University
Xingxin Huang: Fujian Normal University
Ruolan Lin: Fujian Medical University Union Hospital
Deyong Kang: Fujian Medical University Union Hospital
Xingfu Wang: The First Affiliated Hospital of Fujian Medical University
Haohua Tu: University of Illinois at Urbana-Champaign
Wenxi Liu: Fuzhou University
Feng Huang: Fuzhou University
Jianxin Chen: Fujian Normal University

Nature Communications, 2023, vol. 14, issue 1, 1-15

Abstract: Abstract Stitched fluorescence microscope images inevitably exist in various types of stripes or artifacts caused by uncertain factors such as optical devices or specimens, which severely affects the image quality and downstream quantitative analysis. Here, we present a deep learning-based Stripe Self-Correction method, so-called SSCOR. Specifically, we propose a proximity sampling scheme and adversarial reciprocal self-training paradigm that enable SSCOR to utilize stripe-free patches sampled from the stitched microscope image itself to correct their adjacent stripe patches. Comparing to off-the-shelf approaches, SSCOR can not only adaptively correct non-uniform, oblique, and grid stripes, but also remove scanning, bubble, and out-of-focus artifacts, achieving the state-of-the-art performance across different imaging conditions and modalities. Moreover, SSCOR does not require any physical parameter estimation, patch-wise manual annotation, or raw stitched information in the correction process. This provides an intelligent prior-free image restoration solution for microscopists or even microscope companies, thus ensuring more precise biomedical applications for researchers.

Date: 2023
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DOI: 10.1038/s41467-023-41165-1

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