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Background <p>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.</p> Results <p>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.</p> Conclusions <p>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.</p>

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A knowledge-driven deep learning framework for organoid morphological segmentation and characterization

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年10月21日

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  • Yiming Qin,
  • Jiajia Li,
  • Yin Heng,
  • Zheyuan Wang,
  • Dezhi Wu,
  • Mahi Rahman,
  • Pengwei Hu,
  • Tobias Plötz,
  • Alexander Hopp,
  • Nicholas Kurniawan,
  • Mathias Winkel,
  • Philipp Harbach,
  • Chunling Tang,
  • Feng Tan

摘要

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.