HoloPhaseNet: fully automated deep-learning-based hologram reconstruction using a conditional generative adversarial model

Digital in-line holographic microscopy for label-free identification and tracking of biological cells

Digital in-line holographic microscopy (DIHM) is a non-invasive, real-time, label-free technique that captures three-dimensional (3D) positional, orientational, and morphological information from digital holographic images of living biological cells. Unlike conventional microscopies, the DIHM technique enables precise measurements of dynamic behaviors exhibited by living cells within a 3D volume. This review outlines the fundamental principles and comprehensive digital image processing procedures employed in DIHM-based cell tracking methods. In addition, recent applications of DIHM technique for label-free identification and digital tracking of various motile biological cells, including human blood cells, spermatozoa, diseased cells, and unicellular microorganisms, are thoroughly examined. Leveraging artificial intelligence has significantly enhanced both the speed and accuracy of digital image processing for cell tracking and identification. The quantitative data on cell morphology and dynamics captured by DIHM can effectively elucidate the underlying mechanisms governing various microbial behaviors and contribute to the accumulation of diagnostic databases and the development of clinical treatments.

Nanoplastics in Water: Artificial Intelligence-Assisted 4D Physicochemical Characterization and Rapid In Situ Detection

For the first time, we present a much-needed technology for the in situ and real-time detection of nanoplastics in aquatic systems. We show an artificial intelligence-assisted nanodigital in-line holographic microscopy (AI-assisted nano-DIHM) that automatically classifies nano- and microplastics simultaneously from nonplastic particles within milliseconds in stationary and dynamic natural waters, without sample preparation. AI-assisted nano-DIHM identifies 2 and 1% of waterborne particles as nano/microplastics in Lake Ontario and the Saint Lawrence River, respectively. Nano-DIHM provides physicochemical properties of single particles or clusters of nano/microplastics, including size, shape, optical phase, perimeter, surface area, roughness, and edge gradient. It distinguishes nano/microplastics from mixtures of organics, inorganics, biological particles, and coated heterogeneous clusters. This technology allows 4D tracking and 3D structural and spatial study of waterborne nano/microplastics. Independent transmission electron microscopy, mass spectrometry, and nanoparticle tracking analysis validates nano-DIHM data. Complementary modeling demonstrates nano- and microplastics have significantly distinct distribution patterns in water, which affect their transport and fate, rendering nano-DIHM a powerful tool for accurate nano/microplastic life-cycle analysis and hotspot remediation.

On the use of deep learning for phase recovery

Phase recovery (PR) refers to calculating the phase of the light field from its intensity measurements. As exemplified from quantitative phase imaging and coherent diffraction imaging to adaptive optics, PR is essential for reconstructing the refractive index distribution or topography of an object and correcting the aberration of an imaging system. In recent years, deep learning (DL), often implemented through deep neural networks, has provided unprecedented support for computational imaging, leading to more efficient solutions for various PR problems. In this review, we first briefly introduce conventional methods for PR. Then, we review how DL provides support for PR from the following three stages, namely, pre-processing, in-processing, and post-processing. We also review how DL is used in phase image processing. Finally, we summarize the work in DL for PR and provide an outlook on how to better use DL to improve the reliability and efficiency of PR. Furthermore, we present a live-updating resource ( https://github.com/kqwang/phase-recovery ) for readers to learn more about PR.

Biological Cells as Natural Biophotonic Devices: Fundamental and Applications-introduction to the feature issue

This feature issue of Biomedical Optics Express presents a cross-section of interesting and emerging work of relevance to the use of biological cells or microorganisms in optics and photonics. The technologies demonstrated here aim to address challenges to meeting the optical imaging, sensing, manipulating and therapy needs in a natural or even endogenous manner. This collection of 15 papers includes the novel results on designs of optical systems or photonic devices, image-assisted diagnosis and treatment, and manipulation or sensing methods, with applications for both ex vivo and in vivo use. These works portray the opportunities for exploring the field crossing biology and photonics in which a natural element can be functionalized for biomedical applications.