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

Data Pub Date : 2024-02-01 DOI:10.3390/data9020028
Valerija Movcana, Arnis Strods, Karīna Narbute, Fēlikss Rūmnieks, Roberts Rimša, G. Mozolevskis, Maksims Ivanovs, Roberts Kadiķis, Karlis Zviedris, Laura Leja, Anastasija Zujeva, Tamāra Laimiņa, A. Abols
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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.
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用于机器学习和组织模型评估的芯片上器官 (OOC) 图像数据集
芯片上器官(OOC)技术已成为模拟生理环境的开创性方法,为生物医学研究、药物开发和个性化医疗带来了革命性的变化。OOC 平台可提供更贴近生理的微环境,实现对组织的实时监测,从而开发出功能性组织模型。成像方法是日常监测组织发育的最常用方法。基于图像的机器学习是实时增强和监测 OOC 模型的重要工具。这包括对显微镜下生成的图像进行分类,从而提高模型的性能。本文介绍了一个图像数据集,其中包含由不同细胞类型的 OOC 设置生成的细胞图像。自动明视野显微镜装置生成了 3072 幅图像。对于某些图像,提供了细胞类型、播种密度、播种后时间和流速等参数。这些参数和预定义标准有助于评估图像质量和识别潜在伪影。该数据集可作为训练机器学习分类器的基础,用于对 OOC 设置生成的数据进行自动分析,从而提供更可靠的组织模型、OOC 框架内的自动决策过程以及未来的高效研究。
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