Infusion extraction and measurement on CT images based on computer vision and neural network

This paper presents a new approach to the automated detection and quantification of pulmonary emphysema and pneumoconiosis using computed tomography images. The proposed method employs computer vision and neural network algorithms to improve the accuracy and speed of lung diagnosis, as well as the monitoring of emphysema and its changes over time. The study analyzes existing approaches and demonstrates the novelty of the proposed method. The paper reports high accuracy of emphysema extraction and size measurements based on three different patient cases, as evaluated by an expert, and the successful segmentation of pneumosclerosis. The proposed method has the potential to significantly improve medical image segmentation, particularly in the detection and diagnosis of diseases such as Chronic Obstructive Pulmonary Disease (COPD) and COVID-19. The study concludes that the proposed method may also be useful in other areas of medical imaging, contributing to the ongoing effort to develop new and improved methods for medical image analysis and interpretation

Digital Platform of Yenisei Siberia “Siberiana”

Стремительное развитие цифровых технологий меняет облик образовательной и социальной сфер. Исследователи сейчас все чаще предпочитают использовать специализированные цифровые платформы для работы с данными. В статье мы проанализировали существующие региональные цифровые платформы и представили новую платформу под названием «Сибириана». Эта платформа собирает и хранит цифровые коллекции культурного и исторического наследия Енисейской Сибири. «Сибириана» служит одновременно научным хранилищем-агрегатором и платформой для использования, анализа и обработки данных в исследовательских и студенческих проектах. На данный момент платформа находится на стадии развития и совершенствования. Платформа «Сибириана» имеет потенциал стать центром внимания ученых всего мира, драйвером развития исследований о Сибири и частью сети научной цифровой гуманитаристики. Платформа направлена на предоставление исследователям высококачественных образовательных продуктов, которые они могут использовать в своих исследовательских, образовательных и социокультурных проектахThe rapid development of digital technologies is changing the face of the educational and social sphere. Researchers are now increasingly choosing to use specialized digital platforms to work with data. In this article, we analyzed the existing regional digital platforms and introduced a new platform called Sibiriana. This platform collects and stores digital collections of the cultural and historical heritage of Yenisei Siberia. Sibiriana serves both as a scientific repository-aggregator and a platform for the use, analysis and processing of data in research and student projects. At the moment, the platform is at the stage of development and improvement. The Sibiriana platform has the potential to become the center of attention of scientists around the world, the driver of the development of research about Siberia and part of the network of scientific digital humanities. The platform aims to provide researchers with high-quality educational products that they can use in their research, educational and socio-cultural project

Infusion extraction and measurement on CT images based on computer vision and neural network

This paper presents a new approach to the automated detection and quantification of pulmonary emphysema and pneumoconiosis using computed tomography images. The proposed method employs computer vision and neural network algorithms to improve the accuracy and speed of lung diagnosis, as well as the monitoring of emphysema and its changes over time. The study analyzes existing approaches and demonstrates the novelty of the proposed method. The paper reports high accuracy of emphysema extraction and size measurements based on three different patient cases, as evaluated by an expert, and the successful segmentation of pneumosclerosis. The proposed method has the potential to significantly improve medical image segmentation, particularly in the detection and diagnosis of diseases such as Chronic Obstructive Pulmonary Disease (COPD) and COVID-19. The study concludes that the proposed method may also be useful in other areas of medical imaging, contributing to the ongoing effort to develop new and improved methods for medical image analysis and interpretation