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The combined microscopy can be useful in the clinical applications of RCM by providing multiple contrasts. © 2020 Optical Society of America under the terms of the OSA Open Access Publishing Agreement.The tear meniscus contains most of the tear fluid and therefore is a good indicator for the state of the tear film. Previously, we used a custom-built optical coherence tomography (OCT) system to study the lower tear meniscus by automatically segmenting the image data with a thresholding-based segmentation algorithm (TBSA). In this report, we investigate whether the results of this image segmentation algorithm are suitable to train a neural network in order to obtain similar or better segmentation results with shorter processing times. Considering the class imbalance problem, we compare two approaches, one directly segmenting the tear meniscus (DSA), the other first localizing the region of interest and then segmenting within the higher resolution image section (LSA). A total of 6658 images labeled by the TBSA were used to train deep convolutional neural networks with supervised learning. Five-fold cross-validation reveals a sensitivity of 96.36% and 96.43%, a specificity of 99.98% and 99.86% and a Jaccard index of 93.24% and 93.16% for the DSA and LSA, respectively. Average segmentation times are up to 228 times faster than the TBSA. Additionally, we report the behavior of the DSA and LSA in cases challenging for the TBSA and further test the applicability to measurements acquired with a commercially available OCT system. The application of deep learning for the segmentation of the tear meniscus provides a powerful tool for the assessment of the tear film, supporting studies for the investigation of the pathophysiology of dry eye-related diseases. © 2020 Optical Society of America under the terms of the OSA Open Access Publishing Agreement.Ultrasound-switchable fluorescence (USF) is a novel imaging technique that provides high spatial resolution fluorescence images in centimeter-deep biological tissue. Recently, we successfully demonstrated the feasibility of in vivo USF imaging using a frequency-domain photomultiplier tube-based system. In this work, for the first time we carried out in vivo USF imaging via a camera-based USF imaging system. The system acquires a USF signal on a two-dimensional (2D) plane, which facilitates the image acquisition because the USF scanning area can be planned based on the 2D image and provides high USF photon collection efficiency. We demonstrated in vivo USF imaging in the mouse's glioblastoma tumor with multiple targets via local injection. In addition, we designed the USF contrast agents with different particle sizes (70 nm and 330 nm) so that they could bio-distribute to various organs (spleen, liver, and kidney) via intravenous (IV) injections. The results showed that the contrast agents retained stable USF properties in tumors and some organs (spleen and liver). We successfully achieved in vivo USF imaging of the mouse's spleen and liver via IV injections. The USF imaging results were compared with the images acquired from a commercial X-ray micro computed tomography (micro-CT) system. © 2020 Optical Society of America under the terms of the OSA Open Access Publishing Agreement.Human-induced pluripotent stem cell-derived cardiomyocytes (hiPS-CMs) beating can be efficiently characterized by time-lapse quantitative phase imaging (QPIs) obtained by digital holographic microscopy. Particularly, the CM's nucleus section can precisely reflect the associated rhythmic beating pattern of the CM suitable for subsequent beating pattern characterization. In this paper, we describe an automated method to characterize single CMs by nucleus extraction from QPIs and subsequent beating pattern reconstruction and quantification. SH T 04268H However, accurate CM's nucleus extraction from the QPIs is a challenging task due to the variations in shape, size, orientation, and lack of special geometry. To this end, we propose a novel fully convolutional neural network (FCN)-based network architecture for accurate CM's nucleus extraction using pixel classification technique and subsequent beating pattern characterization. Our experimental results show that the beating profile of multiple extracted single CMs is less noisy and more informative compared to the whole image slide. Applying this method allows CM characterization at the single-cell level. Consequently, several single CMs are extracted from the whole slide QPIs and multiple parameters regarding their beating profile of each isolated CM are efficiently measured. © 2020 Optical Society of America under the terms of the OSA Open Access Publishing Agreement.Cholesteatoma of the ear can lead to life-threatening complications and its only treatment is surgery. The smallest remnants of cholesteatoma can lead to recurrence of this disease. Therefore, the optical properties of this tissue are of high importance to identify and remove all cholesteatoma during therapy. In this paper, we determine the absorption coefficient µ a and scattering coefficient µ s ' of cholesteatoma and bone samples in the wavelength range of 250 nm to 800 nm obtained during five surgeries. These values are determined by high precision integrating sphere measurements in combination with an optimized inverse Monte Carlo simulation (iMCS). To conserve the optical behavior of living tissues, the optical spectroscopy measurements are performed immediately after tissue removal and preparation. It is shown that in the near-UV and visible spectrum clear differences exist between cholesteatoma and bone tissue. While µ a is decreasing homogeneously for cholesteatoma, it retains at the high level for bone in the region of 350 nm to 580 nm. Further, the results for the cholesteatoma measurements correspond to published healthy epidermis data. These differences in the optical parameters reveal the future possibility to detect and identify, automatically or semi-automatically, cholesteatoma tissue for active treatment decisions during image-guided surgery leading to a better surgical outcome. © 2020 Optical Society of America under the terms of the OSA Open Access Publishing Agreement.SH T 04268H