Document Type
Article
Subject Area(s)
Organoids; Culture Techniques; Humans; Tissue Engineering; Cell Survival; Deep Learning; Models, Biological
Abstract
BACKGROUND: Organoids have great potential to revolutionize various aspects of biomedical research and healthcare. Researchers typically use the fluorescence-based approach to analyse their dynamics, which requires specialized equipment and may interfere with their growth. Therefore, it is an open challenge to develop a general framework to analyse organoid dynamics under non-invasive and low-resource settings.
RESULTS: In this paper, we present a knowledge-driven deep learning system named TransOrga-plus to automatically analyse organoid dynamics in a non-invasive manner. Given a bright-field microscopic image, TransOrga-plus detects organoids through a multi-modal transformer-based segmentation module. To provide customized and robust organoid analysis, a biological knowledge-driven branch is embedded into the segmentation module which integrates biological knowledge, e.g. the morphological characteristics of organoids, into the analysis process. Then, based on the detection results, a lightweight multi-object tracking module based on the decoupling of visual and identity features is introduced to track organoids over time. Finally, TransOrga-plus outputs the dynamics analysis to assist biologists for further research. To train and validate our framework, we curate a large-scale organoid dataset encompassing diverse tissue types and various microscopic imaging settings. Extensive experimental results demonstrate that our method outperforms all baselines in organoid analysis. The results show that TransOrga-plus provides comparable analytical results to biologists and significantly accelerates organoid work process.
CONCLUSIONS: 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.
Digital Object Identifier (DOI)
Publication Info
Published in BMC Biology, Volume 23, Issue 1, 2025, pages 313-.
APA Citation
Qin, Y., Li, J., Heng, Y., Wang, Z., Wu, D., Rahman, M., Hu, P., Plötz, T., Hopp, A., Kurniawan, N., Winkel, M., Harbach, P., Tang, C., & Tan, F. (2025). A knowledge-driven deep learning framework for organoid morphological segmentation and characterization. BMC Biology, 23(1).https://doi.org/10.1186/s12915-025-02411-8
Rights
© The Author(s) 2025. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.