Valērija Movčana, Arnis Strods, Karīna Narbute, Fēlikss Rūmnieks, Roberts Rimša, Gatis Mozoļevskis, Maksims Ivanovs, Roberts Kadiķis, Kārlis Gustavs Zviedris, Laura Leja, Anastasija Zujeva, Tamāra Laimiņa, Arturs Abols. Organ-On-A-Chip (OOC) Image Dataset for Machine Learning and Tissue Model Evaluation. Data, 9(2), 28 pp. MDPI, 2024.
Bibtex citāts:
Bibtex citāts:
@article{16880_2024,
author = {Valērija Movčana and Arnis Strods and Karīna Narbute and Fēlikss Rūmnieks and Roberts Rimša and Gatis Mozoļevskis and Maksims Ivanovs and Roberts Kadiķis and Kārlis Gustavs Zviedris and Laura Leja and Anastasija Zujeva and Tamāra Laimiņa and Arturs Abols},
title = {Organ-On-A-Chip (OOC) Image Dataset for Machine Learning and Tissue Model Evaluation},
journal = {Data},
volume = {9},
issue = {2},
pages = {28},
publisher = {MDPI},
year = {2024}
}
author = {Valērija Movčana and Arnis Strods and Karīna Narbute and Fēlikss Rūmnieks and Roberts Rimša and Gatis Mozoļevskis and Maksims Ivanovs and Roberts Kadiķis and Kārlis Gustavs Zviedris and Laura Leja and Anastasija Zujeva and Tamāra Laimiņa and Arturs Abols},
title = {Organ-On-A-Chip (OOC) Image Dataset for Machine Learning and Tissue Model Evaluation},
journal = {Data},
volume = {9},
issue = {2},
pages = {28},
publisher = {MDPI},
year = {2024}
}
Anotācija: 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.
Žurnāla kvartile: Q1