Dr. Shufan Yang Profile

Dr. Shufan Yang

at Edinburgh Napier Univ

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Publications (2)

SPIE Journal Paper | 19 August 2022 Open Access

Semisupervised representative learning for measuring epidermal thickness in human subjects in optical coherence tomography by leveraging datasets from rodent models

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

JBO, Vol. 27, Issue 08, 085002, (August 2022) https://doi.org/10.1117/12.10.1117/1.JBO.27.8.085002

KEYWORDS: Data modeling, Image segmentation, Optical coherence tomography, Skin, Human subjects, Tumor growth modeling, Combustion, Data acquisition, Wound healing, Performance modeling

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SPIE Journal Paper | 18 January 2022 Open Access

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

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

JBO, Vol. 27, Issue 01, 015002, (January 2022) https://doi.org/10.1117/12.10.1117/1.JBO.27.1.015002

KEYWORDS: Wound healing, Image segmentation, Optical coherence tomography, Tissues, Skin, Data modeling, Imaging systems, Tissue optics, Signal to noise ratio, Performance modeling

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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.

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