A knowledge-driven deep learning framework for organoid morphological segmentation and characterization
In conclusion, TransOrga-plus integrates the biological expertise with cutting-edge deep learning-based model and enables the non-invasive analysis of various organoids from complex, low-resource, and time-lapse situations.
Authors
- Nicholas A. Kurniawan (ORCID: https://orcid.org/0000-0003-0473-0222)
- Zheyuan Wang (ORCID: https://orcid.org/0000-0001-6364-5074)
- Mathias Winkel (ORCID: https://orcid.org/0000-0002-0345-9701)
- Chunling Tang (ORCID: https://orcid.org/0000-0002-5930-8742)
- Tobias Plötz (ORCID: https://orcid.org/0000-0003-1386-939X)
- Dezhi Wu (ORCID: https://orcid.org/0000-0001-7152-3069)
- Alexander V. Hopp (ORCID: https://orcid.org/0000-0003-1729-1046)
- Philipp H. P. Harbach
- Heng Yin (ORCID: https://orcid.org/0000-0002-8942-7742)
- Yiming Qin (ORCID: https://orcid.org/0000-0002-1552-5139)
- Pengwei Hu (ORCID: https://orcid.org/0000-0001-5974-7932)
- Jiajia Li (ORCID: https://orcid.org/0000-0001-8158-3970)
- Feng Tan
- Mahi Rahman
Institutions
- Merck KGaA, Darmstadt (Germany) (DE)
- Shanghai Jiao Tong University (CN)
- King's College London (GB)
- Xinjiang Technical Institute of Physics & Chemistry (CN)
- Eindhoven University of Technology (NL)
- Tsinghua University (CN)
Publication Details
- Journal
- BMC Biology
- Published
- 2025-10-21
- DOI
- https://doi.org/10.1186/s12915-025-02411-8
- Citations
- 4
- Primary Topic
- Cell Image Analysis Techniques
- Type
- article
- Field-Weighted Citation Impact
- 6.54
Funders
- Merck KGaA
- Science and Technology Commission of Shanghai Municipality