Automatic Assessment of OCT Skin Thickness in Mice Based on Transfer Learning and Attention Mechanisms

Abstract

Optical coherence tomography (OCT) is characterized by high resolution and noninvasiveness; thus, it has been widely used to analyze skin tissues in recent years. Previous studies have evaluated skin OCT images using traditional algorithms with low accuracy for complex tissue structure images. Although a few studies have used deep learning methods to assess tissue structure in OCT images, they lack quantitative assess-ment of deeper skin tissue thickness and are limited to the epidermal layer. Thus, in the present study, we proposed an automated segmentation and quantitative evaluation method. The skin OCT images were first pre-processed, and the attention mechanism was added to U-Net based on transfer learning to segment the images and quantify the thickness of mouse skin tissue structure. The results showed that U-Net combined with the coordinate attention (CA) mechanism had better segmentation performance with 93.94% mean intersection of union (MIoU) value and 96.99% Dice similarity coefficient; the segmentation errors were 0.6 μm, 2.2 μm, 3.8 μm, and 6.0 μm for the epidermis layer, subcutaneous fat layer, muscle fascia layer, and the overall skin tissue structure of mice, respectively. The overall skin tissue thickness of the four mice were 235 ± 20 μm, 264 ± 42 μm, 275 ± 40 μm, and 774 ± 91 μm, respectively. The present study provides a rapid and accurate method for the automated measurement of skin tissue thickness.

Keywords

• Deep learning
• Image segmentation
• Optical coherence tomography
• U-Net