Centre for Digital Telecommunication Technologies, St. Petersburg Electrotechnical University "LETI", St. Petersburg, 197022, Russia.
Artificial Intelligence Research Institute, China University of Mining and Technology, Xuzhou, 221116, China.
Sci Data. 2024 Jun 15;11(1):635. doi: 10.1038/s41597-024-03473-z.
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.
肺动脉高压（PH）是一种综合征，伴随许多不同病因的疾病，与肺循环血管的结构和功能变化的基本机制有关，并表现为肺动脉压力升高。肺循环血管的结构变化是决定 PH 患者预后的主要限制因素。肺动脉分支壁的胶原增厚和不可逆沉积导致疾病迅速进展和治疗效果降低。在这方面，肺循环血管的组织学检查在临床前研究和临床实践中都至关重要。然而，测量平均血管外径、血管壁面积和肥大指数等定量参数需要大量的时间投入，并需要专门的培训来分析显微照片。本工作提出了一种基于深度学习的语义分割技术的肺循环血管病理学评估数据集。该数据集包含 609 张原始血管显微照片、专家测量的数值数据以及每张血管的这些测量的轮廓显微照片。此外，这里我们引用了一个使用 U-Net 语义分割模型提取血管区域的深度学习管道的示例。所提出的数据库将有助于开发用于分析组织学显微照片的新软件解决方案。
