Imaging sebaceous gland using optical coherence tomography with deep learning assisted automatic identification

Segmentation and quantitative analysis of optical coherence tomography (OCT) images of laser burned skin based on deep learning

Evaluation of skin recovery is an important step in the treatment of burns. However, conventional methods only observe the surface of the skin and cannot quantify the injury volume. Optical coherence tomography (OCT) is a non-invasive, non-contact, real-time technique. Swept source OCT uses near infrared light and analyzes the intensity of light echo at different depths to generate images from optical interference signals. To quantify the dynamic recovery of skin burns over time, laser induced skin burns in mice were evaluated using deep learning of Swept source OCT images. A laser-induced mouse skin thermal injury model was established in thirty Kunming mice, and OCT images of normal and burned areas of mouse skin were acquired at day 0, day 1, day 3, day 7, and day 14 after laser irradiation. This resulted in 7000 normal and 1400 burn B-scan images which were divided into training, validation, and test sets at 8:1.5:0.5 ratio for the normal data and 8:1:1 for the burn data. Normal images were manually annotated, and the deep learning U-Net model (verified with PSPNe and HRNet models) was used to segment the skin into three layers: the dermal epidermal layer, subcutaneous fat layer, and muscle layer. For the burn images, the models were trained to segment just the damaged area. Three-dimensional reconstruction technology was then used to reconstruct the damaged tissue and calculate the damaged tissue volume. The average IoU value and f-score of the normal tissue layer U-Net segmentation model were 0.876 and 0.934 respectively. The IoU value of the burn area segmentation model reached 0.907 and f-score value reached 0.951. Compared with manual labeling, the U-Net model was faster with higher accuracy for skin stratification. OCT and U-Net segmentation can provide rapid and accurate analysis of tissue changes and clinical guidance in the treatment of burns.

Deep learning automatically assesses 2-µm laser-induced skin damage OCT images

The present study proposed a noninvasive, automated, in vivo assessment method based on optical coherence tomography (OCT) and deep learning techniques to qualitatively and quantitatively analyze the biological effects of 2-µm laser-induced skin damage at different irradiation doses. Different doses of 2-µm laser irradiation established a mouse skin damage model, after which the skin-damaged tissues were imaged non-invasively in vivo using OCT. The acquired images were preprocessed to construct the dataset required for deep learning. The deep learning models used were U-Net, DeepLabV3+, PSP-Net, and HR-Net, and the trained models were used to segment the damage images and further quantify the damage volume of mouse skin under different irradiation doses. The comparison of the qualitative and quantitative results of the four network models showed that HR-Net had the best performance, the highest agreement between the segmentation results and real values, and the smallest error in the quantitative assessment of the damage volume. Based on HR-Net to segment the damage image and quantify the damage volume, the irradiation doses 5.41, 9.55, 13.05, 20.85, 32.71, 52.92, 76.71, and 97.24 J/cm² corresponded to a damage volume of 4.58, 12.56, 16.74, 20.88, 24.52, 30.75, 34.13, and 37.32 mm³. The damage volume increased in a radiation dose-dependent manner.

5-Aminolaevulinic acid photodynamic therapy suppresses lipid secretion of primary sebocytes through AMPK/SREBP-1 pathway

BACKGROUND

Acne vulgaris is a chronic inflammatory skin disease around pilosebaceous unit. 5-Aminolaevulinic acid photodynamic therapy (ALA-PDT) is an effective therapy for severe acne vulgaris. However, its specific treatment mechanism remains unclear. In the present study, we investigated the potential mechanism of how ALA-PDT induced lipid secretion inhibition in acne vulgaris.

METHODS

Primary human sebocytes and sebaceous gland of golden hamster were treated with/without ALA-PDT. Cell viability was evaluated by Live/Dead Cell assay. Fluorescence microscope was used to observe lipids secretion in sebocytes after Nile red staining. The expression of SREBP-1 after ALA-PDT was evaluated by qRT-PCR. Regulation of ALA-PDT on AMPK/SREBP-1 was evaluated by western blot.

RESULTS

The results showed that ALA-PDT suppressed lipid secretion of primary human sebocytes. In addition, ALA-PDT could inhibit the expression of SREBP-1 in vitro. We also found that ALA-PDT activated AMPK pathway, down-regulating the expression of SREBP-1 in sebocytes after ALA-PDT.

CONCLUSIONS

These findings elucidate that ALA-PDT suppresses lipid secretion through AMPK/SREBP-1 pathway in treatment of acne vulgaris.