Data-driven coordinated attention deep learning for high-fidelity brain imaging denoising and inpainting.

Journal: Journal of biophotonics
Published Date: Jan 2, 2024

Abstract

Deep learning offers promise in enhancing low-quality images by addressing weak fluorescence signals, especially in deep in vivo mouse brain imaging. However, current methods struggle with photon scarcity and noise within in vivo deep mouse brains, and often neglecting tissue preservation. In this study, we propose an innovative in vivo cortical fluorescence image restoration approach, combining signal enhancement, denoising, and inpainting. We curated a deep brain cortical image dataset and developed a novel deep brain coordinate attention restoration network (DeepCAR), integrating coordinate attention with optimized residual networks. Our method swiftly and accurately restores deep cortex images exceeding 800 μm, preserving small-scale tissue structures. It boosts the peak signal-to-noise ratio (PSNR) by 6.94 dB for weak signals and 11.22 dB for large noisy images. Crucially, we validate the effectiveness on external datasets with diverse noise distributions, structural features compared to those in our training data, showcasing real-time high-performance image restoration capabilities.

Authors

  • Chenggui Luo
    Key Laboratory of Optoelectronic Devices and Systems of Guangdong Province and Ministry of Education, College of Physics and Optoelectronic Engineering, Shenzhen University, Shenzhen, China.
  • Wen Pang
    Med-X Research Institute and School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China.
  • Binglin Shen
    Key Laboratory of Optoelectronic Devices and Systems of Guangdong Province and Ministry of Education, College of Physics and Optoelectronic Engineering, Shenzhen University, Shenzhen, China.
  • Zewei Zhao
    Key Laboratory of Optoelectronic Devices and Systems of Guangdong Province and Ministry of Education, College of Physics and Optoelectronic Engineering, Shenzhen University, Shenzhen, China.
  • Shiqi Wang
    Xijing Hospital, Fourth Military Medical University, Xi'an, China.
  • Rui Hu
    School of Automation and Artificial Intelligence, Huazhong University of Science and Technology, Wuhan, 430074, China.
  • Junle Qu
    Shenzhen Key Laboratory of Ultrafast Laser Micro/Nano Manufacturing, Key Laboratory of Optoelectronic Devices and Systems of Ministry of Education/Guangdong Province, College of Physics and Optoelectronic Engineering, Shenzhen University, Shenzhen 518060, China.
  • Bobo Gu
    Med-X Research Institute and School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China.
  • Liwei Liu
    Shenzhen Key Laboratory of Ultrafast Laser Micro/Nano Manufacturing, Key Laboratory of Optoelectronic Devices and Systems of Ministry of Education/Guangdong Province, College of Physics and Optoelectronic Engineering, Shenzhen University, Shenzhen 518060, China.

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