Abstract
Organoids are self-assembled 3D cellular structures that resemble organs structurally and functionally, providing in vitro platforms for molecular and therapeutic studies. Generation of organoids from human cells often requires long and costly procedures with arguably low efficiency. Prediction and selection of cellular aggregates that result in healthy and functional organoids can be achieved by using artificial intelligence-based tools. Transforming images of 3D cellular constructs into digitally processable data sets for training deep learning models requires labeling of morphological boundaries, which often is performed manually. Here, we report an application named OrganoLabeler, which can create large image-based data sets in a consistent, reliable, fast, and user-friendly manner. OrganoLabeler can create segmented versions of images with combinations of contrast adjusting, K-means clustering, CLAHE, binary, and Otsu thresholding methods. We created embryoid body and brain organoid data sets, of which segmented images were manually created by human researchers and compared with OrganoLabeler. Validation is performed by training U-Net models, which are deep learning models specialized in image segmentation. U-Net models, which are trained with images segmented by OrganoLabeler, achieved similar or better segmentation accuracies than the ones trained with manually labeled reference images. OrganoLabeler can replace manual labeling, providing faster and more accurate results for organoid research free of charge.