Super-Resolution Reconstruction of Cytoskeleton Image Based on A-Net Deep Learning Network
Qian Chen,
Haoxin Bai,
Bingchen Che,
Tianyun Zhao,
Ce Zhang,
Kaige Wang,
Jintao Bai,
Wei Zhao
Affiliations
Qian Chen
School of Automation, Northwestern Polytechnical University, Xi’an 710129, China
Haoxin Bai
State Key Laboratory of Photon-Technology in Western China Energy, International Collaborative Center on Photoelectric Technology and Nano Functional Materials, Institute of Photonics & Photon Technology, Northwestern University, Xi’an 710127, China
Bingchen Che
State Key Laboratory of Photon-Technology in Western China Energy, International Collaborative Center on Photoelectric Technology and Nano Functional Materials, Institute of Photonics & Photon Technology, Northwestern University, Xi’an 710127, China
Tianyun Zhao
School of Automation, Northwestern Polytechnical University, Xi’an 710129, China
Ce Zhang
State Key Laboratory of Photon-Technology in Western China Energy, International Collaborative Center on Photoelectric Technology and Nano Functional Materials, Institute of Photonics & Photon Technology, Northwestern University, Xi’an 710127, China
Kaige Wang
State Key Laboratory of Photon-Technology in Western China Energy, International Collaborative Center on Photoelectric Technology and Nano Functional Materials, Institute of Photonics & Photon Technology, Northwestern University, Xi’an 710127, China
Jintao Bai
State Key Laboratory of Photon-Technology in Western China Energy, International Collaborative Center on Photoelectric Technology and Nano Functional Materials, Institute of Photonics & Photon Technology, Northwestern University, Xi’an 710127, China
Wei Zhao
State Key Laboratory of Photon-Technology in Western China Energy, International Collaborative Center on Photoelectric Technology and Nano Functional Materials, Institute of Photonics & Photon Technology, Northwestern University, Xi’an 710127, China
To date, live-cell imaging at the nanometer scale remains challenging. Even though super-resolution microscopy methods have enabled visualization of sub-cellular structures below the optical resolution limit, the spatial resolution is still far from enough for the structural reconstruction of biomolecules in vivo (i.e., ~24 nm thickness of microtubule fiber). In this study, a deep learning network named A-net was developed and shows that the resolution of cytoskeleton images captured by a confocal microscope can be significantly improved by combining the A-net deep learning network with the DWDC algorithm based on a degradation model. Utilizing the DWDC algorithm to construct new datasets and taking advantage of A-net neural network’s features (i.e., considerably fewer layers and relatively small dataset), the noise and flocculent structures which originally interfere with the cellular structure in the raw image are significantly removed, with the spatial resolution improved by a factor of 10. The investigation shows a universal approach for exacting structural details of biomolecules, cells and organs from low-resolution images.
