OrgaQuant: Intestinal Organoid Localization and Quantification Using Deep Convolutional Neural Networks
Organoid cultures are proving to be powerful in vitro models that closely mimic the cellular constituents of their native tissue. The organoids are typically expanded and cultured in a 3D environment using either naturally derived or synthetic extracellular matrices. Assessing the morphology and growth characteristics of these cultures has been difficult due to the many imaging artifacts that accompany the corresponding images. Unlike single cell cultures, there are no reliable segmentation techniques that allow for the localization and quantification of organoids in their 3D culture environment. Here we describe OrgaQuant, a deep convolutional neural network implementation that can locate and quantify the size distribution of intestinal organoids in brightfield images. OrgaQuant is an end-to-end trained neural network that requires no parameter tweaking, thus it can be fully automated to analyze thousands of images with no user intervention. To develop OrgaQuant we created a unique dataset of manually annotated intestinal organoid images and trained an object detection pipeline using TensorFlow. We have made the dataset, trained model and inference scripts publically available along with detailed usage instructions.