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

Multimodal optical systems for clinical oncology

This thesis presents three multimodal optical (light-based) systems designed to improve the capabilities of existing optical modalities for cancer diagnostics and theranostics. Optical diagnostic and therapeutic modalities have seen tremendous success in improving the detection, monitoring, and treatment of cancer. For example, optical spectroscopies can accurately distinguish between healthy and diseased tissues, fluorescence imaging can light up tumours for surgical guidance, and laser systems can treat many epithelial cancers. However, despite these advances, prognoses for many cancers remain poor, positive margin rates following resection remain high, and visual inspection and palpation remain crucial for tumour detection. The synergistic combination of multiple optical modalities, as presented here, offers a promising solution. The first multimodal optical system (Chapter 3) combines Raman spectroscopic diagnostics with photodynamic therapy using a custom-built multimodal optical probe. Crucially, this system demonstrates the feasibility of nanoparticle-free theranostics, which could simplify the clinical translation of cancer theranostic systems without sacrificing diagnostic or therapeutic benefit. The second system (Chapter 4) applies computer vision to Raman spectroscopic diagnostics to achieve spatial spectroscopic diagnostics. It provides an augmented reality display of the surgical field-of-view, overlaying spatially co-registered spectroscopic diagnoses onto imaging data. This enables the translation of Raman spectroscopy from a 1D technique to a 2D diagnostic modality and overcomes the trade-off between diagnostic accuracy and field-of-view that has limited optical systems to date. The final system (Chapter 5) integrates fluorescence imaging and Raman spectroscopy for fluorescence-guided spatial spectroscopic diagnostics. This facilitates macroscopic tumour identification to guide accurate spectroscopic margin delineation, enabling the spectroscopic examination of suspicious lesions across large tissue areas. Together, these multimodal optical systems demonstrate that the integration of multiple optical modalities has potential to improve patient outcomes through enhanced tumour detection and precision-targeted therapies.Open Acces

Biometrics

Biometrics-Unique and Diverse Applications in Nature, Science, and Technology provides a unique sampling of the diverse ways in which biometrics is integrated into our lives and our technology. From time immemorial, we as humans have been intrigued by, perplexed by, and entertained by observing and analyzing ourselves and the natural world around us. Science and technology have evolved to a point where we can empirically record a measure of a biological or behavioral feature and use it for recognizing patterns, trends, and or discrete phenomena, such as individuals' and this is what biometrics is all about. Understanding some of the ways in which we use biometrics and for what specific purposes is what this book is all about

Quantification of tumor heterogeneity using PET/MRI and machine learning

Despite a broad understanding that solid tumors exhibit significant tissue heterogeneity, clinical trials have not seen a remarkable development in techniques that aid in characterizing cancer. Needle biopsies often represent only a partial view of the tumor profile, lacking the ability to comprehensively reflect spatiotemporal phenotypic changes. Recent multimodal multiparametric imaging techniques could provide further valuable insights if the complementary imaging information is sufficiently analyzed. Therefore, in this work I developed and applied machine learning methods on multiparametric positron emission tomography (PET) and magnetic resonance imaging (MRI) datasets, acquired using mice bearing subcutaneous tumors, to obtain a precise spatio-temporal characterization of intratumor heterogeneity

Imaging Sensors and Applications

In past decades, various sensor technologies have been used in all areas of our lives, thus improving our quality of life. In particular, imaging sensors have been widely applied in the development of various imaging approaches such as optical imaging, ultrasound imaging, X-ray imaging, and nuclear imaging, and contributed to achieve high sensitivity, miniaturization, and real-time imaging. These advanced image sensing technologies play an important role not only in the medical field but also in the industrial field. This Special Issue covers broad topics on imaging sensors and applications. The scope range of imaging sensors can be extended to novel imaging sensors and diverse imaging systems, including hardware and software advancements. Additionally, biomedical and nondestructive sensing applications are welcome