Recent advances in computing power triggered the use of artificial intelligence in image analysis in life sciences. To train these algorithms, a large enough set of certified labeled data is required. The trained neural network is then capable of producing accurate instance segmentation results that will then need to be re‐assembled into the original dataset: the entire process requires substantial expertise and time to achieve quantifiable results. To speed‐up the process, from cell organelle detection to quantification across electron microscopy modalities, we propose a deep‐learning based approach for fast automatic outline segmentation (FAMOUS), that involves organelle detection combined with image morphology, and 3D meshing to automatically segment, visualize and quantify cell organelles within volume electron microscopy datasets. From start to finish, FAMOUS provides full segmentation results within a week on previously unseen datasets. FAMOUS was showcased on a HeLa cell dataset acquired using a focused ion beam scanning electron microscope, and on yeast cells acquired by transmission electron tomography.Research Highlights Introducing a rapid, multimodal machine‐learning workflow for the automatic segmentation of 3D cell organelles. Successfully applied to a variety of volume electron microscopy datasets and cell lines. Outperforming manual segmentation methods in time and accuracy. Enabling high‐throughput quantitative cell biology.

中文翻译：

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使用深度学习在体积电子显微镜中自动分割细胞器

计算能力的最新进步引发了人工智能在生命科学图像分析中的应用。为了训练这些算法，需要一组足够大的经过认证的标记数据。经过训练的神经网络能够产生准确的实例分割结果，然后需要将其重新组装到原始数据集中：整个过程需要大量的专业知识和时间才能获得可量化的结果。为了加速这一过程，从细胞器检测到跨电子显微镜模式的量化，我们提出了一种基于深度学习的快速自动轮廓分割（FAMOUS）方法，其中涉及细胞器检测与图像形态学相结合，以及 3D 网格划分以自动分割，可视化和量化体积电子显微镜数据集中的细胞器。从开始到结束，FAMOUS 在一周内提供了以前未见过的数据集的完整分割结果。 FAMOUS 在使用聚焦离子束扫描电子显微镜获取的 HeLa 细胞数据集以及通过透射电子断层扫描获取的酵母细胞数据集上进行了展示。 研究亮点 引入快速、多模式机器学习工作流程，用于 3D 细胞器的自动分割。 成功应用于多种体积电子显微镜数据集和细胞系。 在时间和准确性方面优于手动分割方法。 实现高通量定量细胞生物学。