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in the healthcare market. The article describes general approaches to the formation of M. Porter's competitive strategies, on the basis of which groups of healthcare organizations are identified, in which it is possible to use basic competitive strategiescost leadership, differentiation, focus. The author interpreted M. Porter's generic strategies for health care organizations on the basis of a study of the conceptual structure of competition in the health care market and identified the main strategies based on three competitive advantages -quality of healthcare services, improvement of the process of creating healthcare services, differentiation of healthcare services. Criteria for organizing and grouping competitive marketing strategies for healthcare organizations are proposed: cost orientation, focus on service quality, consumer oriented. The developed classification is based on the study of different models of competition and considers the peculiarities of the medical field. Separately, the author focuses on comparing «Red ocean» and «Blue ocean» strategies and examines the possibilities of introducing streamlined database technology (blockchain) in developing competitive strategies for health care organizations. The advantages of the combination of Blue ocean strategy and blockchain technology are substantiated in the article.

Curvilinear structures frequently appear in microscopy imaging as the object of interest. Crystallographic defects, i.e dislocations, are one of the curvilinear structures that have been repeatedly investigated under transmission electron microscopy (TEM) and their 3D structural information is of great importance for understanding the properties of materials. 3D information of dislocations is often obtained by tomography which is a cumbersome process since it is required to acquire many images with different tilt angles and similar imaging conditions. Although, alternative stereoscopy methods lower the number of required images to two, they still require human intervention and shape priors for accurate 3D estimation. We propose a fully automated pipeline for both detection and matching of curvilinear structures in stereo pairs by utilizing deep convolutional neural networks (CNNs) without making any prior assumption on 3D shapes. In this work, we mainly focus on 3D reconstruction of dislocations from stereo pairs of TEM images.