INVESTIGATION OF FILTERING AND OBJECTS DETECTION ALGORITHMS FOR A MULTIZONE IMAGE SEQUENCE

The problem of detecting objects on a sequence of images with a complex structure is considered. Optimal and quasi-optimal algorithms for processing multidimensional images have been synthesized and investigated. Improved detection efficiency has been obtained by adequately describing real data using doubly stochastic random fields. The possibility of describing Earth remote sensing data using doubly stochastic models is investigated. The possibility of obtaining significant gains when filtering satellite material and detecting extended objects on it due to the adaptive structure of such models and processing time sequence of multizone images as a single multidimensional dataset is shown. The gains for filtering algorithms in the error variance are about 80% comparing single frame processing, and the gains for detecting algorithms in the signal/noise ratio are about 70% comparing single frame processing

Virtual reality simulator for developing spatial skills during retrograde intrarenal pyeloscopy

Introduction. Currently, several virtual reality (VR) simulators have been described for the upper urinary tract stone surgery skill development, including retrograde or antegrade nephroscopy. However, their high cost and the lack of a detailed reconstruction of the intraluminal pelvicalyceal system (PCS) appearance limit their implementation into educational process and clinical practice.Objectives. To develop the approach for VR-reconstruction of the intraluminal appearance of the PCS via head mounted device (HMD), as well as estimate its usefulness for novices to improve spatial orientation during retrograde flexible nephroscopy.Materials & methods. Five residents without experience in self-performing retrograde flexible nephroscopy participated in a 7-day training course on the VR simulator developed, during which each novice studied six variants of the PCS. For the procedure simulation, a silicone kidney model was created with the stone placed in the calyx which was selected randomly in each case. Before and after VR-course, each resident assisted the experienced urologist during simulated retrograde nephroscopy to find the stone placed according to random selection. The nephroscopy time and the number of errors in stone-finding during retrograde flexible nephroscopy were analysed.Results. There was a statistically significant decrease in nephroscopy time (on avg by 17.6 minutes, p = 0.043) and errors to find targeted calyx, which was observed once after the training one resident only.Conclusion. The described VR simulator does not require significant time, technical and financial costs, and is available for wide implementation in the training of young specialists

The satellite images restoring and estimating of parameters by complex structure models

This work considers the algorithm of recovering lost areas on satellite images. It is proposed to use a model with variable parameters because such models can describe real images more adequately. Algorithms based on different models and "start-end" points choice are compared. It is shown that the complex structure models provide more accurate recovery in comparison with the models having constant parameters. It was supposed the simplified algorithm of parameters pseudogradient estimating in doubly stochastic models.В данной работе рассматривается алгоритм восстановления потерянных областей спутниковых изображений. При этом предлагается использовать модели с изменяющимися параметрами, чтобы учесть сложную структуру реальных изображений. Проводится сравнение работы алгоритмы для различных путей восстановления и для моделей различной сложности. Показано, что модели со сложной структурой обеспечивают большую точность восстановления по сравнению с моделями с постоянными параметрами. Предложен упрощенный алгоритм псевдоградиентного оценивания параметров дважды стохастических моделей

Анализ подходов к глубокому обучению для автоматизированного выделения и сегментации предстательной железы: обзор литературы

Background. Delineation of the prostate boundaries represents the initial step in understanding the state of the whole organ and is mainly manually performed, which takes a long time and directly depends on the experience of the radiologists. Automated prostate selection can be carried out by various approaches, including using artificial intelligence and its subdisciplines – machine and deep learning.Aim. To reveal the most accurate deep learning-based methods for prostate segmentation on multiparametric magnetic resonance images.Materials and methods. The search was conducted in July 2022 in the PubMed database with a special clinical query (((AI) OR (machine learning)) OR (deep learning)) AND (prostate) AND (MRI). The inclusion criteria were availability of the full article, publication date no more than five years prior to the time of the search, availability of a quantitative assessment of the reconstruction accuracy by the Dice similarity coefficient (DSC) calculation.Results. The search returned 521 articles, but only 24 papers including descriptions of 33 different deep learning networks for prostate segmentation were selected for the final review. The median number of cases included for artificial intelligence training was 100 with a range from 25 to 365. The optimal DSC value threshold (0.9), in which automated segmentation is only slightly inferior to manual delineation, was achieved in 21 studies.Conclusion. Despite significant achievements in the development of deep learning-based prostate segmentation algorithms, there are still problems and limitations that should be resolved before artificial intelligence can be implemented in clinical practice.Введение. Определение границ предстательной железы является начальным шагом в понимании состояния органа и в основном выполняется вручную, что занимает длительное время и напрямую зависит от опыта рентгенолога. Автоматизация в выделении предстательной железы может быть осуществлена различными подходами, в том числе с помощью искусственного интеллекта и его субдисциплин – машинного и глубокого обучения.Цель работы – детальный анализ литературы для определения наиболее эффективных способов автоматизированной сегментации предстательной железы по снимкам мультипараметрической магнитно-резонансной томографии посредством глубокого обучения.Материалы и методы. Поиск публикаций проводился в июле 2022 г. в поисковой системе PubMed с помощью клинического запроса (((AI) OR (machine learning)) OR (deep learning)) AND (prostate) AND (MRI). Критериями включения были доступность полного текста статьи, дата публикации не более 5 лет на момент поиска, наличие количественной оценки точности реконструкции предстательной железы с помощью коэффициента Серенсена–Дайса (Dice similarity coefficient, DSC).Результаты. В результате поиска найдена 521 статья, из которой в анализ были включены только 24 работы, содержавшие описание 33 различных способов глубокого обучения для сегментации предстательной железы. Медиана количества исследований, включенных для обучения искусственного интеллекта, составила 100 с диапазоном от 25 до 365. Оптимальным значением DSC, при котором автоматизированная сегментация лишь незначительно уступает ручному послойному выделению предстательной железы, составляет 0,9. Так, DSC выше порогового достигнут в описании 21 алгоритма.Заключение. Несмотря на значимые достижения в автоматизированной сегментации предстательной железы с помощью алгоритмов глубокого обучения, до сих пор существует ряд проблем и ограничений, требующих решения для внедрения искусственного интеллекта в клиническую практику

THE THERMODYNAMIC FACTORS AND THE ELECTROCATALYTIC ACTIVITY OF THE TRIPLE Ni-Ti-V ALLOYS

The effect of the enthalpy and entropy factors, Gibbs energy and also electronic  structure of Ni, Ti, V on the electrocatalytic activity of the triple dispersed Ni-Ti-V alloys is shown. It is established, that the presence of the maximum of the electro-catalytic activity of Ni-Ti-V alloys at the variation of the contents of V is caused by opposite action of the enthalpy, entropy and electronic factors

Automated Analysis of X-Ray Images of the Temporomandibular Joint in Patients with Orthognathic Bite and Physiological Occlusion

Objective: to propose a procedure for analyzing the X-ray image of the temporomandibular joint (TMJ) through the automated calculation of linear and angular measurements from craniometry points in comparison with the reference values.Material and methods. Fifty TMJ cone beam computed tomography images were analyzed in 25 volunteers aged 18-25 years with orthognathic bite and physiological occlusion. All the tomography images were analyzed from craniometric points, by using a section corresponding to the midsagittal plane of the TMJ. Angular and linear measurements characterizing the functional capacities of the TMJ were determined. A statistical analysis involved descriptive methods and was carried out by the IBM SPSS 21 statistics.Results. The introduction of automated estimation of TMJ functional capacities from angular and linear measurements made it possible to obtain the following data: the α-angle was 11.99±2.44° for the right TMJ and 12.12±2.78° for the left one; the β-angle was 11.58±2.31° for the right TMJ and 12.42±2.81° for the left one; the γ-angle was 156.41±4.57° for the right TMJ and 155.46±5.50° for the left one. A descriptive statistics checking revealed no gross errors.Conclusion. It is expedient to take into account the findings as reference values to characterize the anatomical and functional state of the TMJ on the x-ray image