Microscopy Image Dataset for Deep Learning-Based Quantitative Assessment of Pulmonary Vascular Changes.

Autor: Sinitca AM; Centre for Digital Telecommunication Technologies, St. Petersburg Electrotechnical University 'LETI', St. Petersburg, 197022, Russia., Lyanova AI; Centre for Digital Telecommunication Technologies, St. Petersburg Electrotechnical University 'LETI', St. Petersburg, 197022, Russia., Kaplun DI; Artificial Intelligence Research Institute, China University of Mining and Technology, Xuzhou, 221116, China. dikaplun@etu.ru.; Department of Automation and Control Processes, St. Petersburg Electrotechnical University 'LETI', St. Petersburg, 197022, Russia. dikaplun@etu.ru., Hassan H; Department of Automation and Control Processes, St. Petersburg Electrotechnical University 'LETI', St. Petersburg, 197022, Russia., Krasichkov AS; Radio Engineering Systems Department, St. Petersburg Electrotechnical University 'LETI', St. Petersburg, 197022, Russia.; Department of Computer Science and Engineering, St. Petersburg Electrotechnical University 'LETI', 197022, Saint Petersburg, Russia., Sanarova KE; Radio Engineering Systems Department, St. Petersburg Electrotechnical University 'LETI', St. Petersburg, 197022, Russia., Shilenko LA; Institute of Experimental Medicine, Almazov National Medical Research Centre, St. Petersburg, 197341, Russia., Sidorova EE; Institute of Experimental Medicine, Almazov National Medical Research Centre, St. Petersburg, 197341, Russia., Akhmetova AA; Institute of Experimental Medicine, Almazov National Medical Research Centre, St. Petersburg, 197341, Russia., Vaulina DD; Institute of Experimental Medicine, Almazov National Medical Research Centre, St. Petersburg, 197341, Russia., Karpov AA; Department of Computer Science and Engineering, St. Petersburg Electrotechnical University 'LETI', 197022, Saint Petersburg, Russia.; Institute of Experimental Medicine, Almazov National Medical Research Centre, St. Petersburg, 197341, Russia.
Jazyk: angličtina
Zdroj: Scientific data [Sci Data] 2024 Jun 15; Vol. 11 (1), pp. 635. Date of Electronic Publication: 2024 Jun 15.
DOI: 10.1038/s41597-024-03473-z
Abstrakt: Pulmonary hypertension (PH) is a syndrome complex that accompanies a number of diseases of different etiologies, associated with basic mechanisms of structural and functional changes of the pulmonary circulation vessels and revealed pressure increasing in the pulmonary artery. The structural changes in the pulmonary circulation vessels are the main limiting factor determining the prognosis of patients with PH. Thickening and irreversible deposition of collagen in the pulmonary artery branches walls leads to rapid disease progression and a therapy effectiveness decreasing. In this regard, histological examination of the pulmonary circulation vessels is critical both in preclinical studies and clinical practice. However, measurements of quantitative parameters such as the average vessel outer diameter, the vessel walls area, and the hypertrophy index claimed significant time investment and the requirement for specialist training to analyze micrographs. A dataset of pulmonary circulation vessels for pathology assessment using semantic segmentation techniques based on deep-learning is presented in this work. 609 original microphotographs of vessels, numerical data from experts' measurements, and microphotographs with outlines of these measurements for each of the vessels are presented. Furthermore, here we cite an example of a deep learning pipeline using the U-Net semantic segmentation model to extract vascular regions. The presented database will be useful for the development of new software solutions for the analysis of histological micrograph.
(© 2024. The Author(s).)
Databáze: MEDLINE
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