Deep neural network automated segmentation of cellular structures in volume electron microscopy

Gallusser, Benjamin and Maltese, Giorgio and Di Caprio, Giuseppe and Vadakkan, Tegy John and Sanyal, Anwesha and Somerville, Elliott and Sahasrabudhe, Mihir and O’connor, Justin and Weigert, Martin and Kirchhausen, Tom (2023) Deep neural network automated segmentation of cellular structures in volume electron microscopy. Journal of Cell Biology, 222 (2). e202208005. ISSN 0021-9525 (https://doi.org/10.1083/jcb.202208005)

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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.

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