Approaching human parity in the quality of automated organoid image segmentation
Existing methods struggle to achieve high-accuracy and consistent automatic segmentation of organoid images across varying experimental conditions. This work proposes a hybrid approach that integrates the general-purpose vision foundation model Segment Anything Model (SAM) with domain-specific segmentation tools, marking the first application of such a combined framework for automated measurement of size and morphology in pluripotent stem cell–derived spheroids. The method delivers stable and accurate segmentation across the majority of tested images, achieving performance on par with or approaching inter-human annotator agreement. This advancement significantly enhances the automation and reliability of organoid image analysis, offering a robust solution for quantitative phenotypic assessment in organoid-based research.