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CEM500K

A large-scale heterogeneous unlabeled cellular electron microscopy image dataset for deep learning methods.

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BioRxiv

Posters

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Enforcing prediction consistency across orthogonal planes significantly improves segmentation of FIB-SEM image volumes by 2D neural networks.

Ryan W Conrad Hanbin Lee and Kedar Narayan

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Efficient skeleton editing in a VR environment facilitates accurate modeling of highly branched mitochondria

Ryan W Conrad, Thomas Ruth, Falko Löffler, Steffen Hadlak, Sebastian Konrad, Christian Götze, Christopher Zugates, Molly McQuilken and Kedar Narayan

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Towards Artifact-free FIB-SEM datasets: Strategies during image acquisition of resin-embedded biological samples

Kedar Narayan, Alexandre Laquerre and Michael Phaneuf

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Super-resolution cryo-fluorescence microscopy of high-pressure-frozen thick samples: a screening study of C. elegans

Irene Y Chang, Mohammad M Rahman, Adam Harned, Orna Cohen-Fix and Kedar Narayan

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Videos

Cryo LM of high-pressure-frozen thick samples (C. elegans)

This video describes a workflow to execute cryo fluorescence microscopy on high-pressure-frozen C. elegans worms. This collaboration with Carl Zeiss GmbH was presented (Poster #1085) at Microscopy & Microanalysis 2020.

“2.5D” Neural Network based segmentation of mitochondria

This video describes a method to enforce prediction consistency across orthogonal planes (hence 2.5D) to improve neural network segmentation of mitochondria in a FIB-SEM image volume. This work was presented (poster #831) at Microscopy & MIcroanalysis 2020.

Towards artifact-free FIB-SEM acquisition

This video describes artifact-free FIB-SEM data acquisition strategies, and accompanied a poster (#535) presented at Microscopy & Microanalysis 2020

Editing mitochondrial skeletons in a virtual reality (VR) environment

In this movie, we describe a module in ArivisVR to import, edit and export corrected mitochondrial skeletons. This work (in collaboration with arivis Inc) was presented (Poster #448) at Microscopy & Microanalysis 2020.