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标题：Microscopic Imaging and Labeling Dataset for the Detection of Pneumocystis jirovecii Using Methenamine Silver Staining Method
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作者：Erick Reyes-Vera ; Juan S. Botero-Valencia ; Karen Arango-Bustamante 等
期刊名称：Data
印刷版ISSN：2306-5729
出版年度：2022
卷号：7
期号：5
页码：1-8
DOI：10.3390/data7050056
语种：English
出版社：MDPI Publishing
摘要：Pneumocystis jirovecii pneumonia is one of the diseases that most affects immunocompro-mised patients today, and under certain circumstances, it can be fatal. On the other hand, moreand more automatic tools based on artiﬁcial intelligence are required every day to help diagnosediseases and thus optimize the resources of the healthcare system. It is therefore important to developtechniques and mechanisms that enable early diagnosis. One of the most widely used techniquesin diagnostic laboratories for the detection of its etiological agent, Pneumocystis jirovecii, is opticalmicroscopy. Therefore, an image dataset of 29 different patients is presented in this work, which canbe used to detect whether a patient is positive or negative for this fungi. These images were takenin at least four random positions on the specimen holder. The dataset consists of a total of 137 RGBimages. Likewise, it contains realistic, annotated, and high-quality microscope images. In addition,we provide image segmentation and labeling that can also be used in numerous studies based onartiﬁcial intelligence implementation. The labeling was also validated by an expert, allowing it to beused as a reference in the training of automatic algorithms with supervised learning methods andthus to develop diagnostic assistance systems. Therefore, the dataset will open new opportunitiesfor researchers working in image segmentation, detection, and classiﬁcation problems related toPneumocystis jirovecii pneumonia diagnosis.Dataset: https://doi.org/10.17605/OSF.IO/WQME8.Dataset License: CC-By Attribution 4.0 International.
关键词：Pneumocystis jirovecii pneumonia;Grocott’s methenamine silver;microscopy;diagnosis;labeling;digital image processing;non-destructive tests