Automatic Quantification of Epidermis Curvature in H&E Stained Microscopic Skin Image of Mice

Changes in the curvature of the epidermis layer is often associated with many skin disorders, such as ichthyoses and generic effects of ageing. Therefore, methods to quantify changes in the curvature are of a scientific and clinical interest. Manual methods to determine curvature are both laborious and intractable to large scale investigations. This paper proposes an automatic algorithm to quantify curvature of microscope images of H&E-stained murine skin. The algorithm can be divided into three key stages. First, skin layers segmentation based on colour deconvolution to separate the original image into three channels of different representations to facilitate segmenting the image into multiple layers, namely epidermis, dermis and subcutaneous layers. The algorithm then further segments the epidermis layer into cornified and basal sub-layers. Secondly, it quantifies the curvature of the epidermis layer by measuring the difference between the epidermis edge and a straight line (theoretical reference line) connecting the two far sides of the epidermis edge. Finally, the curvature measurements extracted from a large number of images of mutant mice are used to identify a list of genes responsible for changes in the epidermis curvature. A dataset of 5714 H&E microscopic images of mutant and wild type mice were used to evaluate the effectiveness of the algorithm

Ghosting artifact reduction of polarization sensitive optical coherence tomography images through wavelet-FFT filtering

Undesirable cross-coupling between polarisation-maintaining (PM) fibers can result in detrimental ghost artefacts within polarisation sensitive optical coherence tomography (PS-OCT) images. Such artefacts combine with coherence noise stripes (originating from Fresnel reflections of optical components), complex-conjugate derived mirror-images and further irregular autocorrelation terms originating from the sample. Together, these artefacts can severely degrade the detected images, making quantitative measurements of the tissue birefringence challenging to perform. In this work, we utilize the recently presented wavelet-FFT filter1 to efficiently suppress these imaging artefacts entirely through post-processing. While the original algorithm was designed to suppress one-dimensional stripe artefacts, we extend this methodology to also facilitate removal of artefacts following a duplicate or inverse (mirror) profile to that of the skin surface. This process does not require any hardware modification of the system and can be applied retroactively to previously acquired OCT images. The performance of this methodology is evaluated by processing artefact-corrupted PS-OCT images of skin consisting of simultaneously detected horizontal and vertical polarized light. The resulting images are used to calculate a phase retardance map within the skin, the profile of which is indicative of localized birefringence. Artefacts in the resulting processed PSOCT images were notably attenuated compared to the unprocessed raw-data, with minimal degradation to the underlying phase retardation information. This should improve the reliability of curve-fitting for measurements of depth-resolved birefringence

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

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