A new approach to optical coherence tomography image analysis using machine learning

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This proposal describes a novel and adventurous pump-priming (pilot) project. The project fits the BBSRC Call for developing "new approaches to the analysis and interpretation of research data in bioimaging, including the development of software tools and algorithms that address challenges arising from emerging new types of data and known problems associated with data handling". Specifically, in this pilot project we will develop a revolutionary approach to analyzing optical coherence tomography (OCT) images, that is, to automatically extract properties that may not be visible to the eye from the images using a mathematical technique called inversion, as well as machine learning techniques and knowledge about the physics of OCT imaging. OCT is a relatively new imaging modality, which provides non-invasive imaging of living tissues based on the principle of optical interferometry. Contrast in OCT is derived from the difference in light scattering and absorption properties of tissue structures. As optical scattering is more varied across soft tissues than either acoustic scattering or x-ray absorption, thus OCT generally provides greater contrast than computed tomography (CT) and ultrasound and can even be sensitive to tissue structural properties at the nanometer length scale. Recent advances in OCT have mainly been driven by applications in biomedical applications such as ophthalmology to study optical nerves development and diagnose glaucoma. These applications rely on the fact that most physiological changes or disease processes affect the optical properties of biological tissue, and this change in tissue optical properties provide the contrast for this imaging technology. Application of OCT is however not limited to the medical domain, e.g., it is also being used in brain imaging, developmental biology, and in tissue engineering. The motivation of the project is the limitation of current approaches to optical coherence tomography (OCT) image analysis, which are subjective and require that the image features to be detected are visible to the eye. However, most often the vital features are invisible to the eye as biological or disease processes are complex and this complexity is reflected in the images. The project will develop a radically different approach to OCT image analysis that extracts tissue optical properties that are invisible to the eye from OCT images to address life and health science research challenges, where little pilot data exists.