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：类器官是自组织形成的三维细胞簇，能够高度模拟体内组织和器官的结构与功能。对类器官形态的量化有助于研究器官发育、药物发现和毒性评估。现代显微成像技术为获取类器官形态特征提供了有力工具，但人工图像分析仍然是一个费时费力的过程。因此，本文提出一套完整的显微图像分析流程，利用SegmentAnything精准勾勒单个类器官边界。此外，我们引入一组形态学属性指标，包括周长、面积、半径、非光滑度和非圆度，使研究人员能够定量且自动地分析类器官结构。为验证本方法的有效性，我们在人诱导多能干细胞（iPSCs）来源的神经上皮（NE）类器官明场图像上进行了测试。自动分析流程所获结果与人工类器官检测及测量结果高度吻合，展示了本方法在加速类器官形态分析方面的能力。