Volume electron microscopy is an important imaging modality in contemporary cell biology. Identification of intracellular structures is a laborious process limiting the effective use of this potentially powerful tool. We resolved this bottleneck with automated segmentation of intracellular substructures in electron microscopy (ASEM), a new pipeline to train a convolutional neural network to detect structures of a wide range in size and complexity. We obtained dedicated models for each structure based on a small number of sparsely annotated ground truth images from only one or two cells. Model generalization was improved with a rapid, computationally effective strategy to refine a trained model by including a few additional annotations. We identified mitochondria, Golgi apparatus, endoplasmic reticulum, nuclear pore complexes, caveolae, clathrin-coated pits, and vesicles imaged by focused ion beam scanning electron microscopy. We uncovered a wide range of membrane–nuclear pore diameters within a single cell and derived morphological metrics from clathrin-coated pits and vesicles, consistent with the classical constant-growth assembly model.

Three-dimensional electron-microscopy is an important imaging modality in contemporary cell biology. Identification of intracellular structures is laborious and time-consuming, however, and seriously impairs effective use of a potentially powerful tool. Resolving this bottleneck is therefore a critical next step in frontier biomedical imaging. We describe Automated Segmentation of intracellular substructures in Electron Microscopy (ASEM), a new pipeline to train a convolutional network to detect structures of wide range in size and complexity. We obtain for each structure a dedicated model based on a small number of sparsely annotated ground truth annotations from only one or two cells. To improve model generalization to different imaging conditions, we developed a rapid, computationally effective strategy to refine an already trained model by including a few additional annotations. We show the successful automated identification of mitochondria, Golgi apparatus, endoplasmic reticulum, nuclear pore complexes, clathrin coated pits and coated vesicles, and caveolae in cells imaged by focused ion beam scanning electron microscopy with quasi-isotropic resolution.