ArticleScientific reports2026
Fast learning-free organoid quantification and tracking with OrganoSeg2.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Organoid Intelligent Morphomics: Decoding the organoid morphome through artificial intelligence from phenotypic quantification to mechanistic insight.Bioactive materials · 2027Review
- Shape factor analysis as a quantitative framework for assessing spheroid and organoid morphology and invasiveness.APL bioengineering · 2026Article
- PCSK5M452I Is a Recessive Hypomorph Exclusive to MCF10DCIS.com Cells.Molecular cancer research : MCR · 2026Article
- IL-6R blockade with tocilizumab disrupts pericyte- and tumor cell-driven IL-6/STAT3 signaling, enhancing docetaxel efficacy in ER+ breast cancer.bioRxiv : the preprint server for biology · 2026Article
- Advances in organoid imaging and automated morphometric analysis: from optical microscopy to computational approaches.Frontiers in cell and developmental biology · 2026Review
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Organoids are routinely imaged by brightfield microscopy at low magnification, but these images are challenging to analyze quantitatively at scale. Given differences in organoid-culture format and image acquisition among research groups, there is a general need for versatile segmentation algorithms that refine for specific applications. Here, we introduce OrganoSeg2, an overhauled software that substantively advances the multi-window adaptive thresholding of its predecessor. OrganoSeg2 gives users access to additional segmentation parameters that were latent in OrganoSeg, and common operations are accelerated ~10-fold. Using data from six organoid types, we find that the generalized segmentation accuracy of OrganoSeg2 surpasses multiple alternatives, including segmenters based on deep learning. OrganoSeg2 adds longitudinal single-organoid tracking and multicolor fluorescence quantification, which we use to examine growth trajectories and radiotherapy responses in luminal breast cancer organoids. OrganoSeg2 is shared freely as installation packages for current users and source code for future developers ( https://github.com/JanesLab/OrganoSeg2 ).
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Registered trials
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