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We consider the problem of localizing and segmenting individual teeth inside 3D Cone-Beam Computed Tomography (CBCT) images. To handle large image sizes we approach this task with a coarse-to-fine framework, where the whole volume is first analyzed as a 33-class semantic segmentation (adults have up to 32 teeth) in coarse resolution, followed by binary semantic segmentation of the cropped region of interest in original resolution. To improve the performance of the challenging 33-class segmentation, we first train the Coarse step model on a large weakly labeled dataset, then fine-tune it on a smaller precisely labeled dataset. The Fine step model is trained with precise labels only. Experiments using our inhouse dataset show significant improvement for both weaklysupervised pretraining and for the addition of the Fine step. Empirically, this framework yields precise teeth masks with low localization errors sufficient for many real-world applications.

The research focus of the current paper are modern algorithms for solving the problem of automatic diagnostic of thorax diseases based on X-ray images. Special attention was paid to image preprocessing, classification together with calculation of features and their comparison in terms of efficiency. Approaches mentioned in this paper are used for development of new algorithm for automatic diagnostic of medical images.

Examinations of a common biological reference organism, (E. coli), demonstrate that NSGA-II is able to provide a series of compressions at various ratios, allows a biologist to examine the organism's connective networks with a measure of certainty of connectiveness. This is due to a novel method of scoring the similarity of the compressed network to the origional during the graph's creation based on the number of false links added to the graph during the compression method.

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