biostudies-literatureGallusser BPCMM Program at Boston Children's HospitalBiogenSANASwiss Federal Institute of Technology LausanneCARIGEST SAMassachusetts Life Sciences CenterNational Institute of General Medical SciencesNIGMS NIH HHSe202208005https://www.ebi.ac.uk/biostudies/studies/S-EPMC9728137biostudies-literatureUnknown222(2)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.The Journal of cell biologyDeep neural network automated segmentation of cellular structures in volume electron microscopy.PMC9728137R35 GM130386GM130386Vadakkan TJO'Connor JKirchhausen TGallusser BSomerville ESahasrabudhe MDi Caprio GWeigert MSanyal AMaltese GfalseDeep neural network automated segmentation of cellular structures in volume electron microscopy.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.2023-01-01T00:00:00Z2023 Feb2025-04-04T21:07:46.199Z2025-04-04T21:07:46.199ZS-EPMC97281373646900110.1083/jcb.202208005