Full-color optically-sectioned imaging by
wide-field microscopy via deep-learning
Posted on 2020-04-17 - 16:35
Wide-field microscopy (WFM) is broadly used in experimental studies of
biological specimens. However, combining the out-of-focus signals with the in-focus plane
reduces the signal-to-noise ratio (SNR) and axial resolution of the image. Therefore,
structured illumination microscopy (SIM) with white light illumination has been used to
obtain full-color 3D images, which can capture high SNR optically-sectioned images with
improved axial resolution and natural specimen colors. Nevertheless, this full-color SIM (FCSIM) has a data acquisition burden for 3D-image reconstruction with a shortened depth-offield, especially for thick samples such as insects and large-scale 3D imaging using stitching
techniques. In this paper, we propose a deep-learning-based method for full-color WFM (FCWFM), i.e., FC-WFM-Deep, which can reconstruct high-quality full-color 3D images with an
extended optical sectioning capability directly from FC-WFM z-stack data. The image quality
achievable with this FC-WFM-Deep method is comparable to the FC-SIM method in terms of
3D information and spatial resolution, while the reconstruction data size is 21 times smaller
and the in-focus depth is doubled. This technique significantly reduces the 3D data
acquisition requirements without losing detail and improves the 3D imaging speed by
extracting the optical sectioning in the depth-of-field. This cost-effective and convenient
method offers a promising tool to observe high-precision color 3D spatial distributions of
biological samples.
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Bai, Chen; Qian, Jia; Dang, Shipei; Peng, Tong; Min, Junwei; Ming, Lei; et al. (2020). Full-color optically-sectioned imaging by
wide-field microscopy via deep-learning. Optica Publishing Group. Collection. https://doi.org/10.6084/m9.figshare.c.4841469.v1
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AUTHORS (8)
CB
Chen Bai
JQ
Jia Qian
SD
Shipei Dang
TP
Tong Peng
JM
Junwei Min
LM
Lei Ming
DD
Dan Dan
BY
Baoli Yao