在细胞生物学领域中，三维细胞聚集体内的细胞分割是一个不断增长的挑战，这是由于显微镜技术的容量和准确性得到了提高。在这里，我们描述了一种流程，用于分割三维细胞聚集体图像。该流程结合了神经网络分割和主动网格。我们将分割方法应用于使用倾斜平面显微镜拍摄的培养的小鼠乳腺导管器官oid图像，在24小时内记录。这是一种高通量光片荧光显微镜技术。我们展示了我们的方法也可以应用于使用旋转盘显微镜拍摄的小鼠胚胎干细胞图像。我们基于细胞核和细胞膜荧光标记分割个别细胞，并跟踪细胞的变化。我们描述了具体指标来量化自动分割的质量。我们的分割流程包括一个Fiji插件，实施主动网格变形，并允许用户创建训练数据，从原始图像数据或神经网络预测自动获取分割网格，并手动校准分割数据以识别和纠正错误。我们的基于主动网格的方法便于分割后处理，纠正以及与神经网络预测的集成。© 2023 Biophysical Society。Elsevier Inc.发行所有版权。

Segmenting cells within cellular aggregates in 3D is a growing challenge in cell biology, due to improvements in capacity and accuracy of microscopy techniques. Here we describe a pipeline to segment images of cell aggregates in 3D. The pipeline combines neural network segmentations with active meshes. We apply our segmentation method to cultured mouse mammary duct organoids imaged over 24 hours with oblique plane microscopy, a high-throughput light-sheet fluorescence microscopy technique. We show that our method can also be applied to images of mouse embryonic stem cells imaged with a spinning disc microscope. We segment individual cells based on nuclei and cell membrane fluorescent markers, and track cells over time. We describe metrics to quantify the quality of the automated segmentation. Our segmentation pipeline involves a Fiji plugin which implement active meshes deformation and allows a user to create training data, automatically obtain segmentation meshes from original image data or neural network prediction, and manually curate segmentation data to identify and correct mistakes. Our active meshes-based approach facilitates segmentation postprocessing, correction, and integration with neural network prediction.Copyright © 2023 Biophysical Society. Published by Elsevier Inc. All rights reserved.