Abstract
Digital holographic microscopy enables the measurement of the quantitative light field information and the visualization of transparent specimens. It can be implemented for complex amplitude imaging and thus for the investigation of biological samples including tissues, dry mass, membrane fluctuation, etc. Currently, deep learning technologies are developing rapidly and have already been applied to various important tasks in the coherent imaging. In this paper, an optimized structural convolution neural network PhaseNet is proposed for the reconstruction of digital holograms, and a deep learning-based holographic microscope using above neural network is implemented for quantitative phase imaging. Living mouse osteoblastic cells are quantitatively measured to demonstrate the capability and applicability of the system.
Highlights
Optical microscope is an effective diagnostic tool in modern healthcare which allows pathologists to clearly and qualitatively observe the details of cells and tissues, and make judgments based on experience
Phase contrast microscopy or differential interference contrast microscopy, which converts the sightless phase shifts introduced by the specimen of interest into observable intensity variations, provide an approach to survey phase specimens without labeling, they cannot provide quantitative phase information on the specimen-induced phase shifts for subsequent accurate diagnosis
An optimized structural convolution neural network PhaseNet is proposed for the reconstruction of digital holograms, and a deep learning-based holographic microscope (DLHM) using PhaseNet is implemented for quantitative phase imaging
Summary
Optical microscope is an effective diagnostic tool in modern healthcare which allows pathologists to clearly and qualitatively observe the details of cells and tissues, and make judgments based on experience. As a typical representative of this technique, digital holographic microscopy (DHM) can be implemented for complex amplitude imaging and be used to investigate transparent specimens, such as biological samples including tissues, dry mass, membrane fluctuation, etc [5,6,7,8]. A common-path digital holographic microscopy based on a beam displacer unit was proposed for quantitative and dynamic phase imaging of biological cells [17]. This implementation reduces the system requirement for the light source coherence, realizes the convenient adjustment of the light beams and achieves an excellent temporal stability. Living mouse osteoblastic cells are quantitatively measured to demonstrate the capability and applicability of the system
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
