Nondestructive three-dimensional (3D) pathology based on high-throughput 3D microscopy holds promise as a complement to traditional hematoxylin and eosin (H&E) stained slide-based two-dimensional (2D) pathology by providing rapid 3D pathological information. However, conventional techniques provided superficial information only due to shallow imaging depth. Herein, we developed open-top two-photon light sheet microscopy (OT-TPLSM) for intraoperative 3D pathology. A two-photon excitation light sheet, generated by 1D scanning of a Bessel beam illuminated the sample and planar imaging was conducted at 400 frames/s max. An imaging depth of 60-100 μm was achieved with long excitation wavelengths, and the image throughput was up to 1 cm2 per 7 min. Cells and extra-cellular matrix were visualized using extrinsic fluorescence and intrinsic second harmonic generation, respectively. OTTPLSM was tested in various human cancer specimens and cancer structures were detected via 3D visualization. OT-TPLSM may have the potential for rapid and precise 3D histopathological examination.
Traditional methods of wound diagnosis have been diagnosed and prescribed by the naked eye of an expert. If the wound segmentation algorithm is applied to the wound diagnosis, the area of wound can be quantitated and used as an auxiliary means of treatment. Even with dramatic development of Deep learning technology in recent years, However, a lack of datasets generally occurs overfitting problem of deep learning model, which leads to poor performance for external datasets. Therefore, we trained the wound segmentation model by adding a new wound dataset in addition to the existing Open dataset, the Diabetic Foot Ulcer Challenge Dataset. Machine learning based methods are used when producing new dataset, ground truth images. Thus, in addition to the manual methods, Gradient Vector Flow machine learning techniques is used for ground-truth image production to reduce the time consumed in vain. The wound segmentation model used in this study is a U-net with residual block combined with cross entropy loss and Dice loss. As a result of the experiment, the wound segmentation accuracy was about 90% for Dice coefficient
Mohs Micrographic Surgery (MMS) needs optical biopsy methods for tumor margin determination. Although confocal microscopy CM) has been used, CM has poor contrast to detect cancer cells with reflection contrast. We developed combined reflectance confocal (RC) & Moxifloxacin based two-photon (MB-TP) microscopy for high contrast. Here, combined microscopy was tested in various skin cancer tissues. Combined microscopy visualized both cell & extracellular matrix. Basal cell carcinoma nests were detected and distinguished from glands. Squamous cell carcinoma was detected with some features. This study showed combined microscopy has potentials for guiding MMS.
Two-photon microscopy (TPM) was used on ex vivo human samples, including the normal skin and various skin cancers. Herein, specific characteristics of TPM images compatible with the histopathologic findings will be presented to evaluate its clinical availability.
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