1. Deep neural network automated segmentation of cellular structures in volume electron microscopy.
- Author
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Gallusser B, Maltese G, Di Caprio G, Vadakkan TJ, Sanyal A, Somerville E, Sahasrabudhe M, O'Connor J, Weigert M, and Kirchhausen T
- Subjects
- Clathrin, Endoplasmic Reticulum ultrastructure, Golgi Apparatus ultrastructure, Mitochondria ultrastructure, Nuclear Pore ultrastructure, Caveolae ultrastructure, Cell Biology, Microscopy, Electron methods, Neural Networks, Computer, Image Processing, Computer-Assisted
- 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., (© 2022 Gallusser et al.)
- Published
- 2023
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