Microscopic Imaging and Labeling Dataset for the Detection of Pneumocystis jirovecii Using Methenamine Silver Staining Method

Erick Reyes-Vera, Juan S. Botero-Valencia, Karen Arango-Bustamante, Alejandra Zuluaga, Tonny W. Naranjo

    Research output: Contribution to journalArticlepeer-review

    Abstract

    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, more and more automatic tools based on artificial intelligence are required every day to help diagnose diseases and thus optimize the resources of the healthcare system. It is therefore important to develop techniques and mechanisms that enable early diagnosis. One of the most widely used techniques in diagnostic laboratories for the detection of its etiological agent, Pneumocystis jirovecii, is optical microscopy. Therefore, an image dataset of 29 different patients is presented in this work, which can be used to detect whether a patient is positive or negative for this fungi. These images were taken in at least four random positions on the specimen holder. The dataset consists of a total of 137 RGB images. 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 on artificial intelligence implementation. The labeling was also validated by an expert, allowing it to be used as a reference in the training of automatic algorithms with supervised learning methods and thus to develop diagnostic assistance systems. Therefore, the dataset will open new opportunities for researchers working in image segmentation, detection, and classification problems related to Pneumocystis jirovecii pneumonia diagnosis.

    Original languageEnglish
    Article number56
    JournalData
    Volume7
    Issue number5
    DOIs
    StatePublished - May 2022

    Bibliographical note

    Funding Information:
    Funding: Research reported in this publication was supported by Corporación Para Investigaciones Biológicas. Erick Reyes-Vera and J. Botero-Valencia acknowledges the support of Instituto Tecnologico Metropolitano through project P21101. The APC was funded by Corporación Para Investigaciones Biológicas, Instituto Tecnologico Metropolitano, and Universidad Pontificia Bolivariana.

    Publisher Copyright:
    © 2022 by the authors. Licensee MDPI, Basel, Switzerland.

    Keywords

    • diagnosis
    • digital image processing
    • Grocott’s methenamine silver
    • labeling
    • microscopy
    • non-destructive tests
    • Pneumocystis jirovecii pneumonia

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