OSCAR is an online ML-powered tool for organoid cell counting using bright-field images

Burnell, Stephanie E.A., Capitani, Lorenzo, Harris, Chloe A., Badder, Luned M., Parker, Alan L. ORCID: https://orcid.org/0000-0002-9302-1761, Wolffs, Kasope, Chen, Yuan, Godkin, Andrew J. ORCID: https://orcid.org/0000-0002-1910-7567 and Gallimore, Awen M. ORCID: https://orcid.org/0000-0001-6675-7004 2025. OSCAR is an online ML-powered tool for organoid cell counting using bright-field images. Cell Reports: Methods , 101251. 10.1016/j.crmeth.2025.101251

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Abstract

Numerous software tools have been published to aid organoid quantification. These tools generate estimates of total organoid number and morphological characteristics in images. However, there remains a need to estimate the number of organoid cells in a well for use in organoid-based experiments (e.g., co-cultures). We present OSCAR (organoid segmentation and cell number approximation using regression), a workflow for estimating organoid cell numbers from bright-field images. Step one is a Mask-R-CNN-based convolutional neural network for identifying organoids in bright-field images and estimating the area of each organoid. Step two is an empirical multiple linear regression model relating the number of cells in an organoid to its area. OSCAR’s estimate of the total number of cells in a well was within ±16% of the real number of organoid cells. OSCAR is an online tool capable of generating this key metric and will contribute to the increased reliability of organoid-based assays.

Item Type:	Article
Date Type:	Published Online
Status:	In Press
Schools:	Schools > Medicine
Publisher:	Cell Press
ISSN:	2667-2375
Date of First Compliant Deposit:	8 December 2025
Date of Acceptance:	5 November 2025
Last Modified:	08 Dec 2025 16:30
URI:	https://orca.cardiff.ac.uk/id/eprint/182994

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