Enhanced U-Net-Based Deep Learning Model for Automated Segmentation of Organoid Images

Organoids have emerged as powerful in vitro models for studying human development, disease mechanisms, and drug responses. A critical aspect of organoid characterisation is monitoring changes in size and morphology during culture; however, extracting these metrics from high-throughput imaging datasets is time-consuming and often inconsistent. Automated deep-learning approaches can overcome this bottleneck by providing accurate and reproducible image analysis. Here, we present an enhanced U-net-based segmentation model that incorporates region-of-interest refinement to improve the delineation of organoid boundaries. The method was validated on bright-field organoid images and demonstrated robust performance, achieving an accuracy of 98.15%, a dice similarity coefficient of 97.19%, and a Jaccard index of 94.53%. Compared with conventional segmentation methods, our model provides superior boundary detection and morphological quantification. These results highlight the potential of this approach as a reliable tool for high-throughput organoid analysis, supporting applications in disease modelling, drug screening, and personalised medicine.