Organ-On-A-Chip (OOC) Image Dataset for Machine Learning and Tissue Model Evaluation
Author:
Movčana Valērija1, Strods Arnis12ORCID, Narbute Karīna1ORCID, Rūmnieks Fēlikss1ORCID, Rimša Roberts2ORCID, Mozoļevskis Gatis2, Ivanovs Maksims3, Kadiķis Roberts3ORCID, Zviedris Kārlis Gustavs3, Leja Laura3ORCID, Zujeva Anastasija3, Laimiņa Tamāra3, Abols Arturs12
Affiliation:
1. Latvian Biomedical Research and Study Centre, LV-1067 Riga, Latvia 2. CellboxLabs Ltd., LV-1063 Riga, Latvia 3. Institute of Electronics and Computer Science (EDI), LV-1006 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.
Funder
European Regional Development Fund
Subject
Information Systems and Management,Computer Science Applications,Information Systems
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