Organ-on-a-Chip (OOC) Image Dataset

Overview: This dataset contains 3000+ images generated from OOC (organ-on-a-chip) setup with different cell types. The images were generated by an automated brightfield microscopy setup; for each image, such parameters as cell type, time after seeding, and class label ('good' or 'bad' sample quality as assessed by a biology expert) are provided. Furthermore, for some images, seeding density and flow rate are given as well. The dataset can be used for training machine learning classifiers for the automated analysis of the data generated with OOC setup, allowing to create more reliable tissue models and automate decision making processes for growing OOC.
The dataset comprises images of OOC samples from the following cell lines:

A549 (human lung adenocarcinoma alveolar basal epithelial cells, CCL-185, ATTC)
Caco-2 (colorectal adenocarcinoma epithelial cells, HTB-37, ATCC)
HPMEC (human pulmonary microvascular endothelial cells; 3000, ScienCell)
HUVEC (human umbilical vein endothelial cells, CRL-1730, ATCC)
NHBE (normal human bronchial epithelial cells, CC-2541, Lonza)
HSAEC (human small airway epithelial cells, PCS-301-010, ATCC)
Structure of the dataset: The dataset is split into three main folders that correspond to the data split for training machine learning models, i.e., 'train', 'val', and 'test'. The train/val/test split is done proportionally with respect to the class labels, cell lines, and time after seeding (see below), yet the data can be split or merged in other ways to suit the needs of prospective users of the dataset. Within each of the main folders, there are a 'bad' and a 'good' folder with the images corresponding to the respective class labels (see 'Overview' above). The images in 'bad' / 'ģood' folders are further subdivided into folders corresponding to respective cell lines, which are in their turn subdivided into folders corresponding to the different times after seeding. Therefore, it is easy to find images of interest, e.g., '4+ days' 'good' images of the cell line A549 from the 'train' dataset. Further information about the images is available in the file 'OOC_datasheet.xlsx'. 
Acknowledgement: The work presented in this paper was supported by the project 'AI-improved organ on chip cultivation for personalised medicine (AimOOC)' (contract with Central Finance and Contracting Agency of Republic of Latvia no. 1.1.1.1/21/A/079; the project is co-financed by REACT-EU funding for mitigating the consequences of the pandemic crisis).