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

作　　者：Kefu Ning Bolin Lu Xiaojun Wang Xiaoyu Zhang Shuo Nie Tao Jiang Anan Li Guoqing Fan Xiaofeng Wang Qingming Luo Hui Gong Jing Yuan 

机构地区：[1]Britton Chance Center for Biomedical Photonics,Wuhan National Laboratory for Optoelectronics,Huazhong University of Science and Technology,Wuhan,China [2]MoE Key Laboratory for Biomedical Photonics,School of Engineering Sciences,Huazhong University of Science and Technology,Wuhan,China [3]HUST-Suzhou Institute for Brainsmatics,Suzhou,China [4]School of Biomedical Engineering,Hainan University,Haikou,China

出　　处：《Light(Science & Applications)》2023年第9期1943-1960,共18页光（科学与应用)（英文版）

基　　金：This work was supported by the National Science and Technology Innovation 2030 Grant(2021ZD0201001);the National Natural Science Foundation of China(Grant No.81827901,T2122015);the CAMS Innovation Fund for Medical Sciences(2019-I2M-5-014);the Fundamental Research Funds for the Central Universities(HUST:2019kfyXMBZ011).

摘　　要：One intrinsic yet critical issue that troubles the field of fluorescence microscopy ever since its introduction is the unmatched resolution in the lateral and axial directions(i.e.,resolution anisotropy),which severely deteriorates the quality,reconstruction,and analysis of 3D volume images.By leveraging the natural anisotropy,we present a deep self-learning method termed Self-Net that significantly improves the resolution of axial images by using the lateral images from the same raw dataset as rational targets.By incorporating unsupervised learning for realistic anisotropic degradation and supervised learning for high-fidelity isotropic recovery,our method can effectively suppress the hallucination with substantially enhanced image quality compared to previously reported methods.In the experiments,we show that Self-Net can reconstruct high-fidelity isotropic 3D images from organelle to tissue levels via raw images from various microscopy platforms,e.g.,wide-field,laser-scanning,or super-resolution microscopy.For the first time,Self-Net enables isotropic whole-brain imaging at a voxel resolution of 0.2×0.2×0.2μm^(3),which addresses the last-mile problem of data quality in single-neuron morphology visualization and reconstruction with minimal effort and cost.Overall,Self-Net is a promising approach to overcoming the inherent resolution anisotropy for all classes of 3D fluorescence microscopy.

关 键 词：DEEP enable RESOLUTION 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]