Summary Patient-derived organoids are increasingly being used in disease modeling, clinical research, drug development, and precision medicine. However, capturing complex organoid behaviors in dense three-dimensional culture environments remains challenging for manual workflows, and bright-field images often contain background artifacts that complicate quantitative analysis. To address this gap, we developed OrgLine, a quantitative analysis pipeline for organoids. Built on a large, curated bright-field image dataset, it couples a detector with a prompt-guided segmentation module. OrgLine recognizes organoids in bright-field images, supports quantitative characterization of morphological phenotypes across developmental stages, and enables accurate instance segmentation for downstream morphometric assessment. Together, these capabilities support more automated and quantitative organoid cultivation workflows.
Deng et al. (Mon,) studied this question.