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

Area of Science:

• Biomedical Research
• Developmental Biology
• Bioinformatics

Background:

• Organoids hold significant promise for biomedical research and healthcare applications.
• Current fluorescence-based methods for analyzing organoid dynamics are resource-intensive and can impede growth.
• A non-invasive, low-resource framework for organoid dynamics analysis remains a critical research challenge.

Purpose of the Study:

• To develop a novel knowledge-driven deep learning system for automated, non-invasive organoid dynamics analysis.
• To overcome the limitations of existing methods by integrating biological knowledge into the analytical framework.
• To accelerate organoid research workflows and assist biologists with data interpretation.

Main Methods:

• A knowledge-driven deep learning system, TransOrga-plus, was developed.
• A multi-modal transformer-based segmentation module detects organoids using bright-field microscopy.
• A biological knowledge-driven branch integrates morphological characteristics for robust analysis.
• A lightweight multi-object tracking module decouples visual and identity features for temporal analysis.

Main Results:

• TransOrga-plus successfully detects and tracks organoids in a non-invasive manner.
• The system integrates biological knowledge, enhancing the accuracy and robustness of organoid analysis.
• Experimental results on a large-scale, diverse dataset demonstrate superior performance compared to baseline methods.
• The framework provides analytical results comparable to human experts and significantly speeds up the research process.

Conclusions:

• TransOrga-plus effectively integrates biological expertise with advanced deep learning.
• The system enables non-invasive analysis of diverse organoids in complex, low-resource, and time-lapse scenarios.
• This approach facilitates broader adoption and accelerates discovery in organoid research.