{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Gallusser B"],"funding":["PCMM Program at Boston Children's Hospital","Biogen","SANA","Swiss Federal Institute of Technology Lausanne","CARIGEST SA","Massachusetts Life Sciences Center","National Institute of General Medical Sciences","NIGMS NIH HHS"],"pagination":["e202208005"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC9728137"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["222(2)"],"pubmed_abstract":["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."],"journal":["The Journal of cell biology"],"pubmed_title":["Deep neural network automated segmentation of cellular structures in volume electron microscopy."],"pmcid":["PMC9728137"],"funding_grant_id":["R35 GM130386","GM130386"],"pubmed_authors":["Vadakkan TJ","O'Connor J","Kirchhausen T","Gallusser B","Somerville E","Sahasrabudhe M","Di Caprio G","Weigert M","Sanyal A","Maltese G"],"additional_accession":[]},"is_claimable":false,"name":"Deep neural network automated segmentation of cellular structures in volume electron microscopy.","description":"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.","dates":{"release":"2023-01-01T00:00:00Z","publication":"2023 Feb","modification":"2025-04-04T21:07:46.199Z","creation":"2025-04-04T21:07:46.199Z"},"accession":"S-EPMC9728137","cross_references":{"pubmed":["36469001"],"doi":["10.1083/jcb.202208005"]}}