Organoids are three-dimensional stem cell–derived cultures that replicate key spatial architecture and functional behaviours of their tissue of origin, making them valuable disease models. Patient-derived organoids preserve the individual's unique characteristics and phenotypes, enabling personalised assessment of treatment efficacy. However, robustly extracting quantitative data from brightfield microscopy remains a significant challenge, as no analysis platform has been widely adopted to overcome key hurdles, including accurate segmentation of individual organoids in dense, confluent cultures and the quantitation of complex phenotypes such as lumen cavity dynamics. This study evaluates five diverse image segmentation approaches to establish a robust method for organoid analysis, using in-house and public, intestinal organoid data. These include Gaussian-blur based adaptive thresholding (Classical), layered adaptive thresholding from a published platform (OrganoSeg2), the generalist transformer-based Segment Anything Model (SAM), and published deep-learning based tools (OrganoID and POST). Algorithm performance was compared on a class (semantic) level and at the individual organoid (instance) segmentation level. Semantically, the performance of the five algorithms were similar (F1≈0.8) except SAM which showed oversegmentation (F1=0.65). The instance level performance decreased globally by ~0.2 F1 compared to semantic. POST (sens=0.84; prec=0.45), OrganoSeg2 (sens=0.76; prec=0.51) and SAM (sens=0.92; prec=0.34) had high sensitivity with lower precision, OrganoID (sens=0.52; prec=0.83) showed the opposite trend whereas Classical (sens=0.58; prec=0.56) was more balanced. These results highlight fundamental trade-offs between sensitivity and precision, demonstrating that current methods show reduced performance for instance-level segmentation in dense organoid cultures motivating the development of more robust approaches.

Co-authors: Shafagh Waters, Fatemeh Vafaee