Imaging Enhancement of Light-Sheet Fluorescence Microscopy via Deep Learning

被引：22

作者：

Bai, Chen ^{[1
]}

Liu, Chao ^{[1
]}

Yu, Xianghua ^{[1
]}

Peng, Tong ^{[1
]}

Min, Junwei ^{[1
]}

Yan, Shaohui ^{[1
]}

Dan, Dan ^{[1
]}

Yao, Baoli ^{[1
]}

机构：

[1] Chinese Acad Sci, State Key Lab Transient Opt & Photon, Xian Inst Opt & Precis Mech, Xian 710068, Shaanxi, Peoples R China

来源：

IEEE PHOTONICS TECHNOLOGY LETTERS | 2019年 / 31卷 / 22期

基金：

中国国家自然科学基金;

关键词：

Convolutional neural networks; residual learning; light-sheet fluorescence microscopy;

D O I：

10.1109/LPT.2019.2948030

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

The complementary beam subtraction (CBS) method can reduce the out-of-focus background and improve the axial resolution in light-sheet fluorescence microscopy (LSFM) via double scanning a Bessel and the complementary beams. With the assistance of a compressed blind deconvolution and denoising (CBDD) algorithm, the noise and blurring incurred during CBS imaging can be further removed. However, this approach requires double scanning and large computational cost. Here, we propose a deep learning-based method for LSFM, which can reconstruct high-quality images directly from the conventional Bessel beam (BB) light-sheet via a single scan. The image quality achievable with this CBS-Deep method is competitive with or better than the CBS-CBDD method, while the speed of image reconstruction is about 100 times faster. Accordingly, the proposed method can significantly improve the practicality of the CBS-CBDD system by reducing both scanning behavior and reconstruction time. The results show that this cost-effective and convenient method enables high-quality LSFM techniques to be developed and applied.

引用

页码：1803 / 1806

页数：4

共 22 条

[1] Compressed Blind Deconvolution and Denoising for Complementary Beam Subtraction Light-Sheet Fluorescence Microscopy [J].