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.
Digital video otoscope is an indispensable tool in otology that allows inspection of the external auditory canal and tympanic membrane. However, existing solutions have limitations in the diagnosis of various ear diseases and portability. Here, we propose a mobile, deep learning-assisted otoscope for low-resource settings. Our deep learning architecture was trained on clinical data to identify and classify various ear diseases. To evaluate our platform, we compared its performance with the device used in the hospital practice. Our preliminary results demonstrated high diagnostic accuracy indicating a strong potential to become a viable screening solution in low-resource, non-specialist settings.
We introduce an advanced color fundus photography using deep learning (DL) architecture for screening glaucoma in low resource setting. The proposed DL architecture is based on a convolutional neural network and trained using clinical image data from color fundus photography and optical coherence tomography. Customized hand-held device integrated with DL model detect and quantify glaucomatous damage in fundus photograph. In validation study, our approach improves the screening capability which cannot be achieved by retinal fundus photography alone. This low-cost handy device with fast-feedback software would be very adequate tool to screen glaucoma in low resource setting.
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.
Optical coherence tomography (OCT) has been used for visualization of morphological change of tissues over time. Although current OCT technology allows the volumetric and high throughput information of tissues, its quantification and analysis still uses time inefficient and tedious process. In order to fully utilize benefits of OCT, it is desired to integrate the intelligent software platform. As deep learning technology is advanced, it has been emerged as the alternative way for quantitative and automated image processing in bio-imaging field including optical imaging. Deep leaning technique is based on the sufficient training data which could overcome the drawback of traditional handcrafted optical image processing algorithms.
In this study, we introduce a novel and intelligent OCT software platform for accurate skin analysis and classification using deep learning module. Our platform is equipped with automated calculations of morphological skin parameters, such as surface roughness, wrinkle depth, volume, and epidermal thickness. To date, most promising tool for quantitative skin analysis is to use a software package of PRIMOS device which relies on three-dimensional camera systems. In order to evaluate our software platform, we compared OCT skin parameters based on deep learning technique and conventional PRIMOS data. Our preliminary study shows that proposed software platform for 3D OCT is a promising tool for accurate, efficient, and quantitative analysis of volumetric skin. It could be also a better alternative than existing PRIMOS solutions to both cosmeceutical and dermatological field.
Deep anterior lamellar keratoplasty (DALK) is an emerging surgical technique for the restoration of corneal clarity and vision acuity. The big-bubble technique in DALK surgery is the most essential procedure that includes the air injection through a thin syringe needle to separate the dysfunctional region of the cornea. Even though DALK is a well-known transplant method, it is still challenged to manipulate the needle inside the cornea under the surgical microscope, which varies its surgical yield. Here, we introduce the DALK protocol based on the position-guided needle and M-mode optical coherence tomography (OCT). Depth-resolved 26-gage needle was specially designed, fabricated by the stepwise transitional core fiber, and integrated with the swept source OCT system. Since our device is feasible to provide both the position information inside the cornea as well as air injection, it enables the accurate management of bubble formation during DALK. Our results show that real-time feedback of needle end position was intuitionally visualized and fast enough to adjust the location of the needle. Through our research, we realized that position-guided needle combined with M-mode OCT is a very efficient and promising surgical tool, which also to enhance the accuracy and stability of DALK.
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