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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.

Dynamic optical coherence elastography (OCE) tracks elastic wave propagation speed within tissue, enabling quantitative three-dimensional imaging of the elastic modulus. We show that propagating mechanical waves are mode converted at interfaces, creating a finite region on the order of an acoustic wavelength where there is not a simple one-to-one correspondence between wave speed and elastic modulus. Depending on the details of a boundary’s geometry and elasticity contrast, highly complex propagating fields produced near the boundary can substantially affect both the spatial resolution and contrast of the elasticity image. We demonstrate boundary effects on Rayleigh waves incident on a vertical boundary between media of different shear moduli. Lateral resolution is defined by the width of the transition zone between two media and is the limit at which a physical inclusion can be detected with full contrast. We experimentally demonstrate results using a spectral-domain OCT system on tissue-mimicking phantoms, which are replicated using numerical simulations. It is shown that the spatial resolution in dynamic OCE is determined by the temporal and spatial characteristics (i.e., bandwidth and spatial pulse width) of the propagating mechanical wave. Thus, mechanical resolution in dynamic OCE inherently differs from the optical resolution of the OCT imaging system.

Damage of collagen fibers in tendons is often directly related to changes in a tendon's mechanical properties. Direct quantitative elasticity measurement of tendons will provide important information in tendon dysfunction diagnosis and treatment assessment. A feasibility study of quantifying the mechanical properties of a degenerated tendon model by a nondestructive imaging modality, which combines optical coherence elastography and acoustic radiation force (ARF) method, is presented. The degenerated tendon model was produced by the partial degradation of chicken tendons through incubation with collagenase at different concentrations and incubation times. A 30-kHz longitudinal ultrasound transducer was used to provide an ARF signal, which was detected by an ultra-high sensitive phase sensitive optical coherence tomography (PhS-OCT) system. The experimental results demonstrate that the combination of ARF method and PhS-OCT can measure the elasticity of tendon quantitatively. The corresponding changes in tendon elasticity due to the application of collagenase have been revealed by this new imaging modality. This method can potentially be used in the assessment of tissue engineering products and in the diagnosis and treatment progression of tendon diseases.

The combined use of surface acoustic wave (SAW) and phase-sensitive optical coherence tomography (PhS-OCT) is useful to evaluate the elasticity of layered biological tissues, such as normal skin. However, the pathological tissue is often originated locally, leading to the alternation of mechanical properties along both axial and lateral directions. We present a feasibility study on whether the SAW technique is sensitive to detect the alternation of mechanical property along the lateral direction within tissue, which is important for clinical utility of this technique to localize diseased tissue. Experiments are carried out on purposely designed tissue phantoms and ex vivo chicken breast samples, simulating the localized change of elasticity. A PhS-OCT system is employed not only to provide the ultra-high sensitive measurement of the generated surface waves on the tissue surface, but also to provide the real time imaging of the tissue to assist the elasticity evaluation of the heterogeneous tissue. The experimental results demonstrate that with PhS-OCT used as a pressure sensor, the SAW is highly sensitive to the elasticity change of the specimen in both vertical and lateral directions with a sensing depth of ∼ 5 mm with our current system setup, thus promising its useful clinical applications where the quantitative elasticity of localized skin diseases is needed to aid in diagnosis and treatment.