Image segmentation methods for automated morphological analysis of organoids

Organoids are complex, three dimensional, self-organizing cell cultures which manifest organ-like features and represent a powerful platform for studying human disease and developing treatment options. Organoid development usually begins with spheroids derived from pluripotent stem cells (iPSCs), and is characterized by dynamic morphological and cellular organization, which mimic some aspects of organ development. To study these rapid changes over the course of organoid development, advanced imaging and analytical tools are critical.

In this work, we have implemented computer vision and machine learning techniques to measure the size and shape of developing spheroids. Many tools require a significant amount of task-specific training data, but recent advances in “0-shot” methods allow for image segmentation without any specific retraining.

We report on the performance of several tools by testing them on a subset of the data and comparing with the results of manual image segmentation. We show that combining a purpose-built image segmenter with a general-purpose segmentation tool provides more accurate and consistent results than either individual method.