Organoids are self-organized 3D cell clusters that closely mimic the architecture and function of in vivo tissues and organs. Quantification of organoid morphology helps in studying organ development, drug discovery, and toxicity assessment. Recent microscopy techniques provide a potent tool to acquire organoid morphology features, but manual image analysis remains a labor and time-intensive process. Thus, this paper proposes a comprehensive pipeline for microscopy analysis that leverages the SegmentAnything to precisely demarcate individual organoids. Additionally, we introduce a set of morphological properties, including perimeter, area, radius, non-smoothness, and non-circularity, allowing researchers to analyze the organoid structures quantitatively and automatically. To validate the effectiveness of our approach, we conducted tests on bright-field images of human induced pluripotent stem cells (iPSCs) derived neural-epithelial (NE) organoids. The results obtained from our automatic pipeline closely align with manual organoid detection and measurement, showcasing the capability of our proposed method in accelerating organoids morphology analysis.

翻译：类器官是自组织的三维细胞簇，能够密切模拟体内组织和器官的结构与功能。对类器官形态的定量分析有助于研究器官发育、药物发现及毒性评估。近年来显微成像技术为获取类器官形态特征提供了有力工具，但人工图像分析仍是一项劳动密集且耗时的过程。为此，本文提出了一套完整的显微图像分析流程，利用SegmentAnything实现单个类器官的精确分割。此外，我们引入了一组形态学参数，包括周长、面积、半径、非平滑度和非圆度，使研究人员能够自动定量分析类器官结构。为验证该方法的有效性，我们基于人诱导多能干细胞衍生的神经上皮类器官明场图像进行了测试。实验结果表明，本自动流程获得的检测结果与人工类器官检测及测量结果高度吻合，充分展现了我们提出的方法在加速类器官形态分析方面的能力。