ArticleBioengineering (Basel, Switzerland)2025
Enhanced U-Net-Based Deep Learning Model for Automated Segmentation of Organoid Images.
Article in Bioengineering (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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Who cites it
4 citing papers in PubMed.
- Organoid Intelligent Morphomics: Decoding the organoid morphome through artificial intelligence from phenotypic quantification to mechanistic insight.Bioactive materials · 2027Review
- Deep Learning-Based Prediction of Epithelial Cytokine Responses for the Selection of Functionally Consistent Airway Organoids.Biomimetics (Basel, Switzerland) · 2026Article
- [Current status and challenges of artificial intelligence and organoid technologies in precision diagnosis and treatment of gastrointestinal stromal tumors].Zhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences · 2026Review
- Artificial Intelligence in Organoid-Based Disease Modeling: A New Frontier in Precision Medicine.Biomimetics (Basel, Switzerland) · 2025Review
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Organoids have emerged as powerful in vitro models for studying human development, disease mechanisms, and drug responses. A critical aspect of organoid characterisation is monitoring changes in size and morphology during culture; however, extracting these metrics from high-throughput imaging datasets is time-consuming and often inconsistent. Automated deep-learning approaches can overcome this bottleneck by providing accurate and reproducible image analysis. Here, we present an enhanced U-net-based segmentation model that incorporates region-of-interest refinement to improve the delineation of organoid boundaries. The method was validated on bright-field organoid images and demonstrated robust performance, achieving an accuracy of 98.15%, a dice similarity coefficient of 97.19%, and a Jaccard index of 94.53%. Compared with conventional segmentation methods, our model provides superior boundary detection and morphological quantification. These results highlight the potential of this approach as a reliable tool for high-throughput organoid analysis, supporting applications in disease modelling, drug screening, and personalised medicine.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.