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© 2020 Optical Society of America under the terms of the OSA Open Access Publishing Agreement.Fluorescent observation of cells generally suffers from the limited axial resolution due to the elongated point spread function of the microscope optics. Consequently, three-dimensional imaging results in axial resolution that is several times worse than the transversal. The optical solutions to this problem usually require complicated optics and extreme spatial stability. A straightforward way to eliminate anisotropic resolution is to fuse images recorded from multiple viewing directions achieved mostly by the mechanical rotation of the entire sample. In the presented approach, multiview imaging of single cells is implemented by rotating them around an axis perpendicular to the optical axis by means of holographic optical tweezers. selleck products For this, the cells are indirectly trapped and manipulated with special microtools made with two-photon polymerization. The cell is firmly attached to the microtool and is precisely manipulated with 6 degrees of freedom. The total control over the cells' position allows for its multiview fluorescence imaging from arbitrarily selected directions. The image stacks obtained this way are combined into one 3D image array with a multiview image processing pipeline resulting in isotropic optical resolution that approaches the lateral diffraction limit. The presented tool and manipulation scheme can be readily applied in various microscope platforms. © 2020 Optical Society of America under the terms of the OSA Open Access Publishing Agreement.Accurate identification and segmentation of choroidal neovascularization (CNV) is essential for the diagnosis and management of exudative age-related macular degeneration (AMD). Projection-resolved optical coherence tomographic angiography (PR-OCTA) enables both cross-sectional and en face visualization of CNV. However, CNV identification and segmentation remains difficult even with PR-OCTA due to the presence of residual artifacts. In this paper, a fully automated CNV diagnosis and segmentation algorithm using convolutional neural networks (CNNs) is described. This study used a clinical dataset, including both scans with and without CNV, and scans of eyes with different pathologies. Furthermore, no scans were excluded due to image quality. In testing, all CNV cases were diagnosed from non-CNV controls with 100% sensitivity and 95% specificity. The mean intersection over union of CNV membrane segmentation was as high as 0.88. By enabling fully automated categorization and segmentation, the proposed algorithm should offer benefits for CNV diagnosis, visualization monitoring. © 2020 Optical Society of America under the terms of the OSA Open Access Publishing Agreement.We propose a novel method and system that utilizes a popular smartphone to realize hyperspectral imaging for analyzing skin morphological features and monitoring hemodynamics. The imaging system works based on a built-in RGB camera and flashlight on the smartphone. We apply Wiener estimation to transform the acquired RGB-mode images into "pseudo"-hyperspectral images with 16 wavebands, covering a visible range from 470nm to 620nm. The processing method uses weighted subtractions between wavebands to extract absorption information caused by specific chromophores within skin tissue, mainly including hemoglobin and melanin. Based on the extracted absorption information of hemoglobin, we conduct real-time monitoring experiments in the skin to measure heart rate and to observe skin activities during a vascular occlusion event. Compared with expensive hyperspectral imaging systems, the smartphone-based system delivers similar results but with very-high imaging resolution. link2 Besides, it is easy to operate, very cost-effective and has a wider customer base. The use of an unmodified smartphone to realize hyperspectral imaging promises a possibility to bring a hyperspectral analysis of skin out from laboratory and clinical wards to daily life, which may also impact on healthcare in low resource settings and rural areas. © 2020 Optical Society of America under the terms of the OSA Open Access Publishing Agreement.Recent studies in mechanobiology have revealed the importance of cellular and extracellular mechanical properties in regulating cellular function in normal and disease states. Although it is established that cells should be investigated in a three-dimensional (3-D) environment, most techniques available to study mechanical properties on the microscopic scale are unable to do so. In this study, for the first time, we present volumetric images of cellular and extracellular elasticity in 3-D biomaterials using quantitative micro-elastography (QME). We achieve this by developing a novel strain estimation algorithm based on 3-D linear regression to improve QME system resolution. We show that QME can reveal elevated elasticity surrounding human adipose-derived stem cells (ASCs) embedded in soft hydrogels. link3 We observe, for the first time in 3-D, further elevation of extracellular elasticity around ASCs with overexpressed TAZ; a mechanosensitive transcription factor which regulates cell volume. Our results demonstrate that QME has the potential to study the effects of extracellular mechanical properties on cellular functions in a 3-D micro-environment. © 2020 Optical Society of America under the terms of the OSA Open Access Publishing Agreement.This manuscript reports on a closed-form solution determining the personalized required shape of a new intraocular lens able to remove spherical aberration and coma of a pseudophakic eye. The proposed analytical method, within the framework of the Seidel theory of third-order optical aberrations, considers corneal conicities, fourth-order aspheric surface of the intraocular optics, pupil-shift effect and ocular kappa angle. © 2020 Optical Society of America under the terms of the OSA Open Access Publishing Agreement.The adaptive optics (AO) technique is widely used to compensate for ocular aberrations and improve imaging resolution. However, when affected by intraocular scatter, speckle noise, and other factors, the quality of the retinal image will be degraded. To effectively improve the image quality without increasing the imaging system's complexity, the post-processing method of image deblurring is adopted. In this study, we proposed a conditional adversarial network-based method for directly learning an end-to-end mapping between blurry and restored AO retinal images. The proposed model was validated on synthetically generated AO retinal images and real retinal images. The restoration results of synthetic images were evaluated with the metrics of peak signal-to-noise ratio (PSNR), structural similarity (SSIM), perceptual distance, and error rate of cone counting. Moreover, the blind image quality index (BIQI) was used as the no-reference image quality assessment (NR-IQA) algorithm to evaluate the restoration results on real AO retinal images. The experimental results indicate that the images restored by the proposed method have sharper quality and higher signal-to-noise ratio (SNR) when compared with other state-of-the-art methods, which has great practical significance for clinical research and analysis. © 2020 Optical Society of America under the terms of the OSA Open Access Publishing Agreement.Optical coherence tomography (OCT) is susceptible to the coherent noise, which is the speckle noise that deteriorates contrast and the detail structural information of OCT images, thus imposing significant limitations on the diagnostic capability of OCT. In this paper, we propose a novel OCT image denoising method by using an end-to-end deep learning network with a perceptually-sensitive loss function. The method has been validated on OCT images acquired from healthy volunteers' eyes. The label images for training and evaluating OCT denoising deep learning models are images generated by averaging 50 frames of respective registered B-scans acquired from a region with scans occurring in one direction. The results showed that the new approach can outperform other related denoising methods on the aspects of preserving detail structure information of retinal layers and improving the perceptual metrics in the human visual perception. © 2020 Optical Society of America under the terms of the OSA Open Access Publishing Agreement.Protein-based drugs have been developed to treat a variety of conditions and assays use immobilized capture proteins for disease detection. Freeze-drying is currently the standard for the preservation of proteins, but this method is expensive and requires lengthy processing times. Anhydrous preservation in a trehalose amorphous solid matrix offers a promising alternative to freeze-drying. Light assisted drying (LAD) is a processing method to create an amorphous trehalose matrix. Proteins suspended in a trehalose solution are dehydrated using near-infrared laser light. The laser radiation accelerates drying and as water is removed the trehalose forms a protective matrix. In this work, LAD samples are characterized to determine the crystallization kinetics of the trehalose after LAD processing and the distribution of amorphous trehalose in the samples. These characteristics influence the long-term stability of the samples. Polarized light imaging revealed that LAD processed samples are stable against crystallization during low-humidity storage at room temperature. Scanning white light interferometry and Raman spectroscopy indicated that trehalose was present across samples in an amorphous form. In addition, differential scanning microcalorimetry was used to measure the thermodynamic characteristics of the protein lysozyme after LAD processing. These results demonstrate that LAD does not change the properties of this protein. © 2020 Optical Society of America under the terms of the OSA Open Access Publishing Agreement.Stimulated Raman scattering (SRS) microscopy is a promising technique for studying tissue structure, physiology, and function. Similar to other nonlinear optical imaging techniques, SRS is severely limited in imaging depth due to the turbidity and heterogeneity of tissue, regardless of whether imaging in the transmissive or epi mode. While this challenge is well known, important imaging parameters (namely maximum imaging depth and imaging signal to noise ratio) have rarely been reported in the literature. It is also important to compare epi mode and transmissive mode imaging to determine the best geometry for many tissue imaging applications. In this manuscript we report the achievable signal sizes and imaging depths using a simultaneous epi/transmissive imaging approach in four different murine tissues; brain, lung, kidney, and liver. For all four cases we report maximum signal sizes, scattering lengths, and achievable imaging depths as a function of tissue type and sample thickness. We report that for murine brain samples thinner than 2 mm transmissive imaging provides better results, while samples 2 mm and thicker are best imaged with epi imaging. We also demonstrate the use of a CNN-based denoising algorithm to yield a 40 µm (24%) increase in achievable imaging depth. © 2020 Optical Society of America under the terms of the OSA Open Access Publishing Agreement.