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

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

doi:10.1117/1.jbo.27.1.015002

Abstract

.SignificanceIn 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.AimWe 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.ApproachFive 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.ResultsOur 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 -score, 0.875 at mean intersection over union, 0.933 at Dice similarity coefficient, and 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.ConclusionsThe 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.

Highlights

The skin is the largest organ of the human body and provides essential functions to maintain homeostasis of the body
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
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

Summary

Introduction

The skin is the largest organ of the human body and provides essential functions to maintain homeostasis of the body. All the wounds are covered by an epithelium (as an obstacle) administered by several complex events emanating from the epithelium itself and by the temporal recruitment into the wound bed for immune cells.[5,6] Inability to re-epithelialize is a hallmark of chronic non-healing wounds.[7] Note that the epidermis detection, such as the epidermal thickness, is an essential indicator to judge whether the re-epithelialization process is normal.[8] Formation of a scab is an essential indicator, commonly formed in the coagulation and inflammation phases, to provide structural stability to the wound and prevent exsanguination.[9] to design effective treatments, further precise analysis of epidermal restoration and scar formation/loss during wound healing is required

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