Monitoring hair follicle development is important to interpret hair pathophysiology and follicular-related diseases such as alopecia, folliculitis, acne, and keratosis pilaris. We introduce label-free visualization of hair follicles based on optical coherence microscopy (OCM) and quantitative analysis of the structure using OCM imaging data. The hair follicle regeneration of mice was monitored using our home-built serial OCM. Through this process, we obtained the volumetric anatomy and functional feature of the hair follicle. We also quantified the hair follicle structure including hair root, bulb, papilla, and follicle wall based on OCM data. Our result provides insight into the hair cycle investigation.
Surgical microscopes provide clear visualization of the tissue and have been increasingly used in operating rooms. Going further from this, we developed optical coherence tomography (OCT), and optical coherence microscopy (OCM) integrated surgical microscope to offer superficial and sub-surface tissue information simultaneously. With the use of an optical switch, OCT and OCM view can be freely converted. After surgical resection of cancer with the guidance of OCT, we performed high-resolution whole cancer imaging with OCM for margin detection. Our proposed system can be a promising tool for intraoperative applications and increase the accuracy of the operation.
we introduce automated serial OCM toward statistical 3D digital histopathology. Our research is the extension of previous work in order to enhance the process of imaging acquisition. Our approach has three unique features, (1) surface tracking, (2) single body and automated system combined vibratome and microscopic imaging head, and (3) selection of magnification. In validation test, various mouse organs were imaged and quantified at the region of interest which presented less labor and shorten image acquisition time compared to previous works.
Optical imaging techniques with physical tissue sectioning have become indispensable tools. However, acquiring volumetric anatomy of multiple organs and statistical studies remain a difficult challenge due to light scattering and long data acquisition period. Here, we propose a novel protocol for the high-throughput and quantitative analysis of 3D mouse organs using Coherence gating imaging (CGI) with tissue clearing. For statistical analysis, we also applied deep learning and outcomes were compared with computed tomography. Our preliminary results can improve imaging depth as well corresponding acquisition time, which would be promising tool for 3D digital histopathology.
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