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Deep self-learning enables fast, high-fidelity isotropic resolution restoration for volumetric fluorescence microscopy

Medicine and Health

Deep self-learning enables fast, high-fidelity isotropic resolution restoration for volumetric fluorescence microscopy

K. Ning, B. Lu, et al.

Discover the groundbreaking research by Kefu Ning and colleagues as they unveil Self-Net, an innovative deep self-learning approach that revolutionizes axial resolution in fluorescence microscopy using lateral images. This remarkable technique improves image quality and advances whole-brain imaging resolutions to 0.2 x 0.2 x 0.2 µm³, enhancing our ability to visualize single-neuron morphology with unprecedented clarity.

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Playback language: English
Abstract
Fluorescence microscopy suffers from resolution anisotropy, where axial resolution is significantly lower than lateral resolution. This paper introduces Self-Net, a deep self-learning method that leverages the natural anisotropy to improve axial resolution using lateral images from the same dataset. By combining unsupervised learning for realistic anisotropic degradation and supervised learning for high-fidelity isotropic recovery, Self-Net suppresses hallucination and enhances image quality. Experiments demonstrate its effectiveness across various microscopy platforms, enabling isotropic whole-brain imaging at 0.2 x 0.2 x 0.2 µm³ resolution, significantly improving single-neuron morphology visualization and reconstruction.
Publisher
Light: Science & Applications
Published On
Jan 31, 2023
Authors
Kefu Ning, Bolin Lu, Xiaojun Wang, Xiaoyu Zhang, Shuo Nie, Tao Jiang, Anan Li, Guoqing Fan, Xiaofeng Wang, Qingming Luo, Hui Gong, Jing Yuan
Tags
Fluorescence Microscopy
Axial Resolution
Image Quality
Deep Learning
Whole-Brain Imaging
Single-Neuron Morphology

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