DECSEFE-Org: a hierarchical AI-based framework for automatic DEtection, Classification, SEgmentation, and Feature Extraction of Organoids

In recent years, the emergence of organoid-related technology has transformed the landscape of biomedical research by providing near-physiological models that closely mimic human tissue. Intestinal organoids are three-dimensional structures derived from intestinal stem cells state that offer new potential for disease modeling, drug testing, and personalized medicine. However, the complexity of these heterogeneous models requires innovative solutions to optimize the organoids’ characterization and monitoring. From a technological perspective, the automated analysis of intestinal organoid images is essential for high-throughput screening, yet remains challenging due to morphological variability and imaging conditions. To overcome these challenges, this work proposes DECSEFE-Org, a hierarchical and modular framework that combines AI-based models for real-time organoid detection (YOLOv7), classification (DenseNet169), and segmentation (SAM). The pipeline further includes feature extraction to quantify morphological parameters and explainable AI (XAI) modules based on SHAP to support biological interpretation in decision-making processes. The framework was tested on a publicly labeled organoids dataset and achieved 86.5% accuracy in classification and 0.92 Dice score in segmentation. By using DECSEFE-Org is possible to accelerate drug response studies and improve treatment efficacy evaluation, overcoming the speed and accuracy of traditional methods. Experimental validation demonstrates the pipeline’s ability to provide near-real-time results, scalable analysis with low-latency in detection and segmentation, while maintaining interpretability and high-throughput suitability, opening the way for automated and scalable analysis of organoids in biomedical research.