Deep-learning approach for automated thickness measurement of epithelial tissue and scab using optical coherence tomography

• Published in: Journal of biomedical optics Vol. 27; no. 1; p. 015002
• Main Authors: Ji, Yubo, Yang, Shufan, Zhou, Kanheng, Rocliffe, Holly R, Pellicoro, Antonella, Cash, Jenna L, Wang, Ruikang, Li, Chunhui, Huang, Zhihong
• Format: Journal Article
• Language: English
• Published: United States Society of Photo-Optical Instrumentation Engineers 01.01.2022; S P I E - International Society for
• Subjects: Algorithms; Animals; Artificial neural networks; Computer architecture; Cross-Sectional Studies; Deep Learning; Epidermis; Epidermis - diagnostic imaging; Hair; Image processing; Image segmentation; Imaging techniques; In vivo methods and tests; Machine learning; Medical imaging; Medical research; Methods; Neural networks; Neural Networks, Computer; Optical Coherence Tomography; Pathogens; Physical factors; Quantitative analysis; Real time; Rodents; Skin; Thickness measurement; Tissues; Tomography; Tomography, Optical Coherence; Wound healing; United Kingdom > UK; wound healing; re-epithelialization; deep-learning network; scab; optical coherence tomography; epidermis
• ISSN: 1083-3668; 1560-2281
• DOI: 10.1117/1.JBO.27.1.015002

Significance: In order to elucidate therapeutic treatment to accelerate wound healing, it is crucial to understand the process underlying skin wound healing, especially re-epithelialization. Epidermis and scab detection is of importance in the wound healing process as their thickness is a vital indicator to judge whether the re-epithelialization process is normal or not. Since optical coherence tomography (OCT) is a real-time and non-invasive imaging technique that can perform a cross-sectional evaluation of tissue microstructure, it is an ideal imaging modality to monitor the thickness change of epidermal and scab tissues during wound healing processes in micron-level resolution. Traditional segmentation on epidermal and scab regions was performed manually, which is time-consuming and impractical in real time. Aim: We aim to develop a deep-learning-based skin layer segmentation method for automated quantitative assessment of the thickness of in vivo epidermis and scab tissues during a time course of healing within a rodent model. Approach: Five convolution neural networks were trained using manually labeled epidermis and scab regions segmentation from 1000 OCT B-scan images (assisted by its corresponding angiographic information). The segmentation performance of five segmentation architectures was compared qualitatively and quantitatively for validation set. Results: Our results show higher accuracy and higher speed of the calculated thickness compared with human experts. The U-Net architecture represents a better performance than other deep neural network architectures with 0.894 at F1-score, 0.875 at mean intersection over union, 0.933 at Dice similarity coefficient, and 18.28  μm at an average symmetric surface distance. Furthermore, our algorithm is able to provide abundant quantitative parameters of the wound based on its corresponding thickness maps in different healing phases. Among them, normalized epidermal thickness is recommended as an essential hallmark to describe the re-epithelialization process of the rodent model. Conclusions: The automatic segmentation and thickness measurements within different phases of wound healing data demonstrates that our pipeline provides a robust, quantitative, and accurate method for serving as a standard model for further research into effect of external pharmacological and physical factors.