OrgaQuant: Human Intestinal Organoid Localization and Quantification Using Deep Convolutional Neural Networks
Name
s41598-019-48874-y.pdf
Description
Published version
Size
3.18 MB
Format
Adobe PDF
Checksum (MD5)
9589f6aadff5c0f49372bfb2f0a35c55
Author(s)
Kassis, Timothy
Hernandez Gordillo, Victor
Langer, Ronit
Griffin, Linda G.
Date Issued
August 28, 2019
Journal
Scientific reports
Publisher
Springer Science and Business Media LLC
Citation
Kassis, Timothy, Victor Hernandez Gordillo, Ronit Langer, & Linda G. Griffin. "OrgaQuant: Human Intestinal Organoid Localization and Quantification Using Deep Convolutional Neural Networks." Scientific reports 9 (2019): 12479 © 2019 The Author(s)
Version
Final published version
Abstract
Organoid cultures are proving to be powerful in vitro models that closely mimic the cellular constituents of their native tissue. 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 automated 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 human 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 human intestinal organoid images with bounding boxes and trained an object detection pipeline using TensorFlow. We have made the dataset, trained model and inference scripts publicly available along with detailed usage instructions.
Subjects
Multidisciplinary
MIT Department
Massachusetts Institute of Technology. Department of Biological Engineering
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution 4.0 International license
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1038/s41598-019-48874-y
