IDEAS home Printed from https://ideas.repec.org/a/gam/jdataj/v9y2024i2p28-d1331295.html
   My bibliography  Save this article

Organ-On-A-Chip (OOC) Image Dataset for Machine Learning and Tissue Model Evaluation

Author

Listed:
  • Valērija Movčana

    (Latvian Biomedical Research and Study Centre, LV-1067 Riga, Latvia)

  • Arnis Strods

    (Latvian Biomedical Research and Study Centre, LV-1067 Riga, Latvia
    CellboxLabs Ltd., LV-1063 Riga, Latvia)

  • Karīna Narbute

    (Latvian Biomedical Research and Study Centre, LV-1067 Riga, Latvia)

  • Fēlikss Rūmnieks

    (Latvian Biomedical Research and Study Centre, LV-1067 Riga, Latvia)

  • Roberts Rimša

    (CellboxLabs Ltd., LV-1063 Riga, Latvia)

  • Gatis Mozoļevskis

    (CellboxLabs Ltd., LV-1063 Riga, Latvia)

  • Maksims Ivanovs

    (Institute of Electronics and Computer Science (EDI), LV-1006 Riga, Latvia)

  • Roberts Kadiķis

    (Institute of Electronics and Computer Science (EDI), LV-1006 Riga, Latvia)

  • Kārlis Gustavs Zviedris

    (Institute of Electronics and Computer Science (EDI), LV-1006 Riga, Latvia)

  • Laura Leja

    (Institute of Electronics and Computer Science (EDI), LV-1006 Riga, Latvia)

  • Anastasija Zujeva

    (Institute of Electronics and Computer Science (EDI), LV-1006 Riga, Latvia)

  • Tamāra Laimiņa

    (Institute of Electronics and Computer Science (EDI), LV-1006 Riga, Latvia)

  • Arturs Abols

    (Latvian Biomedical Research and Study Centre, LV-1067 Riga, Latvia
    CellboxLabs Ltd., LV-1063 Riga, Latvia)

Abstract

Organ-on-a-chip (OOC) technology has emerged as a groundbreaking approach for emulating the physiological environment, revolutionizing biomedical research, drug development, and personalized medicine. OOC platforms offer more physiologically relevant microenvironments, enabling real-time monitoring of tissue, to develop functional tissue models. Imaging methods are the most common approach for daily monitoring of tissue development. Image-based machine learning serves as a valuable tool for enhancing and monitoring OOC models in real-time. This involves the classification of images generated through microscopy contributing to the refinement of model performance. This paper presents an image dataset, containing cell images generated from OOC setup with different cell types. There are 3072 images generated by an automated brightfield microscopy setup. For some images, parameters such as cell type, seeding density, time after seeding and flow rate are provided. These parameters along with predefined criteria can contribute to the evaluation of image quality and identification of potential artifacts. This dataset can be used as a basis for training machine learning classifiers for automated data analysis generated from an OOC setup providing more reliable tissue models, automated decision-making processes within the OOC framework and efficient research in the future.

Suggested Citation

  • Valērija Movčana & Arnis Strods & Karīna Narbute & Fēlikss Rūmnieks & Roberts Rimša & Gatis Mozoļevskis & Maksims Ivanovs & Roberts Kadiķis & Kārlis Gustavs Zviedris & Laura Leja & Anastasija Zujeva &, 2024. "Organ-On-A-Chip (OOC) Image Dataset for Machine Learning and Tissue Model Evaluation," Data, MDPI, vol. 9(2), pages 1-10, February.
  • Handle: RePEc:gam:jdataj:v:9:y:2024:i:2:p:28-:d:1331295
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2306-5729/9/2/28/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2306-5729/9/2/28/
    Download Restriction: no
    ---><---

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jdataj:v:9:y:2024:i:2:p:28-:d:1331295. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.