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bioRxiv · 10.1101/2020.01.05.895003

Dense cellular segmentation using 2D-3D neural network ensembles for electron microscopy

Guay, M.·Emam, Z.·Anderson, A.·Aronova, M.·Leapman, R. D.
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Abstract

Cell biologists can now build 3D models from segmentations of electron microscopy (EM) images, but accurate manual segmentation of densely-packed organelles across gigavoxel image volumes is infeasible. Here, we introduce 2D-3D neural network ensembles that produce dense cellular segmentations at scale, with accuracy levels that outperform baseline methods and approach those of human annotators.

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Guay, M., Emam, Z., Anderson, A., Aronova, M., Leapman, R. D.. 2020-01-06. Dense cellular segmentation using 2D-3D neural network ensembles for electron microscopy. https://doi.org/10.1101/2020.01.05.895003

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