@article {68887, title = {Volume electron microscopy.}, journal = {Nature Reviews: Methods Primers}, volume = {2}, year = {2022}, month = {2022 Jul 07}, pages = {51}, abstract = {

Life exists in three dimensions, but until the turn of the century most electron microscopy methods provided only 2D image data. Recently, electron microscopy techniques capable of delving deep into the structure of cells and tissues have emerged, collectively called volume electron microscopy (vEM). Developments in vEM have been dubbed a quiet revolution as the field evolved from established transmission and scanning electron microscopy techniques, so early publications largely focused on the bioscience applications rather than the underlying technological breakthroughs. However, with an explosion in the uptake of vEM across the biosciences and fast-paced advances in volume, resolution, throughput and ease of use, it is timely to introduce the field to new audiences. In this Primer, we introduce the different vEM imaging modalities, the specialized sample processing and image analysis pipelines that accompany each modality and the types of information revealed in the data. We showcase key applications in the biosciences where vEM has helped make breakthrough discoveries and consider limitations and future directions. We aim to show new users how vEM can support discovery science in their own research fields and inspire broader uptake of the technology, finally allowing its full adoption into mainstream biological imaging.

}, issn = {2662-8449}, doi = {10.1038/s43586-022-00131-9}, author = {Peddie, Christopher J and Genoud, Christel and Kreshuk, Anna and Meechan, Kimberly and Micheva, Kristina D and Narayan, Kedar and Pape, Constantin and Parton, Robert G and Schieber, Nicole L and Schwab, Yannick and Titze, Benjamin and Verkade, Paul and Aubrey, Aubrey and Collinson, Lucy M} } @article {49220, title = {Synaptic cleft segmentation in non-isotropic volume electron microscopy of the complete Drosophila brain.}, journal = {Medical Image Computing and Computer Assisted Intervention {\textendash} MICCAI 2018}, year = {2018}, month = {2018 Sep 26}, pages = {317-25}, abstract = {

Neural circuit reconstruction at single synapse resolution is increasingly recognized as crucially important to decipher the function of biological nervous systems. Volume electron microscopy in serial transmission or scanning mode has been demonstrated to provide the necessary resolution to segment or trace all neurites and to annotate all synaptic connections.\ 
Automatic annotation of synaptic connections has been done successfully in near isotropic electron microscopy of vertebrate model organisms. Results on non-isotropic data in insect models, however, are not yet on par with human annotation.\ 
We designed a new 3D-U-Net architecture to optimally represent isotropic fields of view in non-isotropic data. We used regression on a signed distance transform of manually annotated synaptic clefts of the CREMI challenge dataset to train this model and observed significant improvement over the state of the art.\ 
We developed open source software for optimized parallel prediction on very large volumetric datasets and applied our model to predict synaptic clefts in a 50 tera-voxels dataset of the complete Drosophila brain. Our model generalizes well to areas far away from where training data was available.

}, doi = {10.1007/978-3-030-00934-2_36}, author = {Heinrich, Larissa and Funke, Jan and Pape, Constantin and Nunez-Iglesias, Juan and Saalfeld, Stephan} } @article {48254, title = {Multicut brings automated neurite segmentation closer to human performance.}, journal = {Nature Methods}, volume = {14}, year = {2017}, month = {2017 Jan 31}, pages = {101-102}, issn = {1548-7105}, doi = {10.1038/nmeth.4151}, author = {Beier, Thorsten and Pape, Constantin and Rahaman, Nasim and Prange, Timo and Berg, Stuart and Bock, Davi D and Cardona, Albert and Knott, Graham W and Plaza, Stephen M and Scheffer, Louis K and Koethe, Ullrich and Kreshuk, Anna and Hamprecht, Fred A} }