Deep Learning Approach for Automated Thickness Measurement of Epithelial Tissue and Scab using Optical Coherence Tomography

Ji, Yubo; Yang, Shufan; Zhou, Kanheng; Rocliffe, Holly R.; Pellicoro, Antonella; Cash, Jenna L.; Wang, Ruikang; Li, Chunhui; Huang, Zhihong

Abstract

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: 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 murine model.
Approach: Five convolution neural networks (CNN) were trained using manually labelled epidermis and scab regions segmentation from 1000 OCT B-scan images (assisted by its corresponding angiographic information). The segmentation performance of five segmentation architectures were 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 a 0.894 at F1-score, 0.875 at mean IOU, 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 mapping in different healing phases. Among them, normalized epidermis thickness is recommended as an essential hallmark to describe the re-epithelialization process of the mouse 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.

Citation

Ji, Y., Yang, S., Zhou, K., Rocliffe, H. R., Pellicoro, A., Cash, J. L., …Huang, Z. (2022). Deep Learning Approach for Automated Thickness Measurement of Epithelial Tissue and Scab using Optical Coherence Tomography. Journal of Biomedical Optics, 27(1), https://doi.org/10.1117/1.JBO.27.1.015002

• Journal Article Type: Article
• Acceptance Date: Nov 23, 2021
• Online Publication Date: Jan 18, 2022
• Publication Date: 2022-01
• Deposit Date: Nov 24, 2021
• Publicly Available Date: Jan 18, 2022
• Print ISSN: 1083-3668
• Publisher: Society of Photo-optical Instrumentation Engineers
• Peer Reviewed: Peer Reviewed
• Volume: 27
• Issue: 1
• DOI: https://doi.org/10.1117/1.JBO.27.1.015002
• Keywords: OCT, deep learning network, wound healing, re-epithelialization, epidermis, scab
• Public URL: http://researchrepository.napier.ac.uk/Output/2823987