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Autofluorescence-Directed Confocal Endomicroscopy in conjunction with the Three-Biomarker Cell Can easily Inform Management Judgements in Barrett's Esophagus.
Optical sectioning has been widely employed for inhibiting out-of-focus backgrounds in three-dimensional (3D) imaging of biological samples. However, point scanning imaging or multiple acquisitions for wide-field optical sectioning in epi-illumination microscopy remains time-consuming for large-scale imaging. In this paper, we propose a single-scan optical sectioning method based on the hybrid illumination (HiLo) algorithm with a line-illumination strategy. Our method combines HiLo background inhibition with confocal slit detection. It thereby offers a higher optical sectioning capability than wide-field HiLo and line-confocal imaging without extra modulation and multiple data acquisition. To demonstrate the optical-sectioning capability of our system, we imaged a thin fluorescent plane and different fluorescence-labeled mouse tissue. Our method shows an excellent background inhibition in thick tissue and thus potentially provides an alternative tool for 3D imaging of large-scale biological tissue.Retinal image-based eye motion measurement from scanned ophthalmic imaging systems, such as scanning laser ophthalmoscopy, has allowed for precise real-time eye tracking at sub-micron resolution. However, the constraints of real-time tracking result in a high error tolerance that is detrimental for some eye motion measurement and imaging applications. We show here that eye motion can be extracted from image sequences when these constraints are lifted, and all data is available at the time of registration. Our approach identifies and discards distorted frames, detects coarse motion to generate a synthetic reference frame and then uses it for fine scale motion tracking with improved sensitivity over a larger area. We demonstrate its application here to tracking scanning laser ophthalmoscopy (TSLO) and adaptive optics scanning light ophthalmoscopy (AOSLO), and show that it can successfully capture most of the eye motion across each image sequence, leaving only between 0.1-3.4% of non-blink frames untracked, while simultaneously minimizing image distortions induced from eye motion. These improvements will facilitate precise measurement of fixational eye movements (FEMs) in TSLO and longitudinal tracking of individual cells in AOSLO.Currently, the cochlear implantation procedure mainly relies on using a hand lens or surgical microscope, where the success rate and surgery time strongly depend on the surgeon's experience. Therefore, a real-time image guidance tool may facilitate the implantation procedure. In this study, we performed a systematic and quantitative analysis on the optical characterization of ex vivo mouse cochlear samples using two swept-source optical coherence tomography (OCT) systems operating at the 1.06-µm and 1.3-µm wavelengths. The analysis results demonstrated that the 1.06-µm OCT imaging system performed better than the 1.3-µm OCT imaging system in terms of the image contrast between the cochlear conduits and the neighboring cochlear bony wall structure. However, the 1.3-µm OCT imaging system allowed for greater imaging depth of the cochlear samples because of decreased tissue scattering. In addition, we have investigated the feasibility of identifying the electrode of the cochlear implant within the ex vivo cochlear sample with the 1.06-µm OCT imaging. The study results demonstrated the potential of developing an image guidance tool for the cochlea implantation procedure as well as other otorhinolaryngology applications.Open-top light-sheet microscopy (OT-LSM) is a specialized microscopic technique for high throughput cellular imaging of large tissue specimens including optically cleared tissues by having the entire optical setup below the sample stage. Current OT-LSM systems had relatively low axial resolutions by using weakly focused light sheets to cover the imaging field of view (FOV). In this report, open-top axially swept LSM (OTAS-LSM) was developed for high-throughput cellular imaging with improved axial resolution. OTAS-LSM swept a tightly focused excitation light sheet across the imaging FOV using an electro tunable lens (ETL) and collected emission light at the focus of the light sheet with a camera in the rolling shutter mode. OTAS-LSM was developed by using air objective lenses and a liquid prism and it had on-axis optical aberration associated with the mismatch of refractive indices between air and immersion medium. The effects of optical aberration were analyzed by both simulation and experiment, and the image resolutions were under 1.6µm in all directions. The newly developed OTAS-LSM was applied to the imaging of optically cleared mouse brain and small intestine, and it demonstrated the single-cell resolution imaging of neuronal networks. OTAS-LSM might be useful for the high-throughput cellular examination of optically cleared large tissues.Automated lesion segmentation is one of the important tasks for the quantitative assessment of retinal diseases in SD-OCT images. Recently, deep convolutional neural networks (CNN) have shown promising advancements in the field of automated image segmentation, whereas they always benefit from large-scale datasets with high-quality pixel-wise annotations. Unfortunately, obtaining accurate annotations is expensive in both human effort and finance. In this paper, we propose a weakly supervised two-stage learning architecture to detect and further segment central serous chorioretinopathy (CSC) retinal detachment with only image-level annotations. selleck inhibitor Specifically, in the first stage, a Located-CNN is designed to detect the location of lesion regions in the whole SD-OCT retinal images, and highlight the distinguishing regions. To generate available a pseudo pixel-level label, the conventional level set method is employed to refine the distinguishing regions. In the second stage, we customize the active-contour loss function in deep networks to achieve the effective segmentation of the lesion area. A challenging dataset is used to evaluate our proposed method, and the results demonstrate that the proposed method consistently outperforms some current models trained with a different level of supervision, and is even as competitive as those relying on stronger supervision. To our best knowledge, we are the first to achieve CSC segmentation in SD-OCT images using weakly supervised learning, which can greatly reduce the labeling efforts.
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