Center for Molecular Microscopy, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA; Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Frederick, MD 21702, USA.
Center for Molecular Microscopy, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA; Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Frederick, MD 21702, USA.
Cell Syst. 2023 Jan 18;14(1):58-71.e5. doi: 10.1016/j.cels.2022.12.006.
Mitochondria are extremely pleomorphic organelles. Automatically annotating each one accurately and precisely in any 2D or volume electron microscopy (EM) image is an unsolved computational challenge. Current deep learning-based approaches train models on images that provide limited cellular contexts, precluding generality. To address this, we amassed a highly heterogeneous ∼1.5 × 10 image 2D unlabeled cellular EM dataset and segmented ∼135,000 mitochondrial instances therein. MitoNet, a model trained on these resources, performs well on challenging benchmarks and on previously unseen volume EM datasets containing tens of thousands of mitochondria. We release a Python package and napari plugin, empanada, to rapidly run inference, visualize, and proofread instance segmentations. A record of this paper's transparent peer review process is included in the supplemental information.
线粒体是极其多样的细胞器。在任何二维或体积电子显微镜 (EM) 图像中自动准确和精确地注释每一个线粒体都是一个未解决的计算挑战。当前基于深度学习的方法在提供有限细胞上下文的图像上训练模型，排除了通用性。为了解决这个问题，我们收集了一个高度异质的约 1.5×10 张二维未标记细胞 EM 数据集，并对其中的约 135000 个线粒体实例进行了分割。MitoNet 是在这些资源上训练的模型，在具有挑战性的基准测试和以前看不见的包含数万个线粒体的体积 EM 数据集上表现良好。我们发布了一个 Python 包和 napari 插件 empanada，用于快速运行推理、可视化和校对实例分割。本文的透明同行评审过程记录包含在补充信息中。
