Intestinal epithelial organoids recapitulate many of the in vivo features of the intestinal epithelium, thus representing excellent research models. Morphology of the organoids based on light-microscopy images is used as a proxy to assess the biological state of the intestinal epithelium. Currently, organoid classification is manual and, therefore, subjective and time consuming, hampering large-scale quantitative analyses. Here, we describe Tellu, an object-detector algorithm trained to classify cultured intestinal organoids. Tellu was trained by manual annotation of >20,000 intestinal organoids to identify cystic non-budding organoids, early organoids, late organoids and spheroids. Tellu can also be used to quantify the relative organoid size, and can classify intestinal organoids into these four subclasses with accuracy comparable to that of trained scientists but is significantly faster and without bias. Tellu is provided as an open, user-friendly online tool to benefit the increasing number of investigations using organoids through fast and unbiased organoid morphology and size analysis.
Keywords: Automation; Classifier; Intestinal organoid; Object detection.
© 2023. Published by The Company of Biologists Ltd.