Gallusser, BenjaminMaltese, GiorgioDi Caprio, GiuseppeVadakkan, Tegy JohnSanyal, AnweshaSomerville, ElliottSahasrabudhe, MihirO'Connor, JustinWeigert, MartinKirchhausen, Tom2023-03-132023-03-132023-03-132022-12-0510.1083/jcb.202208005https://infoscience.epfl.ch/handle/20.500.14299/195869WOS:000935583200001Volume 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. Gallusser et al. present a new pipeline to train a convolutional neural network for rapid and efficient automated detection of intracellular structures of wide range in size and complexity imaged by volume electron microscopy.Cell BiologyCell Biologyenergy minimizationclathrinendocytosistext::journal::journal article::research article