@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 {67226, title = {Deep learning enables fast and dense single-molecule localization with high accuracy}, journal = {Nature Methods}, volume = {18}, year = {2021}, month = {2021 Sep 03}, abstract = {

Single-molecule localization microscopy (SMLM) has had remarkable success in imaging cellular structures with nanometer resolution, but the need for activating only single isolated emitters limits imaging speed and labeling density. Here, we overcome this major limitation using deep learning. We developed DECODE, a computational tool that can localize single emitters at high density in 3D with highest accuracy for a large range of imaging modalities and conditions. In a public software benchmark competition, it outperformed all other fitters on 12 out of 12 data-sets when comparing both detection accuracy and localization error, often by a substantial margin. DECODE allowed us to take live-cell SMLM data with reduced light exposure in just 3 seconds and to image microtubules at ultra-high labeling density. Packaged for simple installation and use, DECODE will enable many labs to reduce imaging times and increase localization density in SMLM.Competing Interest StatementThe authors have declared no competing interest.

}, doi = {10.1101/2020.10.26.355164}, url = {https://www.biorxiv.org/content/early/2020/10/26/2020.10.26.355164}, author = {Speiser, Artur and M{\"u}ller, Lucas-Raphael and Matti, Ulf and Obara, Christopher J. and Legant, Wesley R. and Kreshuk, Anna and Macke, Jakob H. and Ries, Jonas and Turaga, Srinivas C.} } @article {65178, title = {ilastik: interactive machine learning for (bio)image analysis.}, journal = {Nature Methods}, volume = {16}, year = {2019}, month = {2019 Sep 30}, pages = {1226-32}, abstract = {

We present ilastik, an easy-to-use interactive tool that brings machine-learning-based (bio)image analysis to end users without substantial computational expertise. It contains pre-defined workflows for image segmentation, object classification, counting and tracking. Users adapt the workflows to the problem at hand by interactively providing sparse training annotations for a nonlinear classifier. ilastik can process data in up to five dimensions (3D, time and number of channels). Its computational back end runs operations on-demand wherever possible, allowing for interactive prediction on data larger than RAM. Once the classifiers are trained, ilastik workflows can be applied to new data from the command line without further user interaction. We describe all ilastik workflows in detail, including three case studies and a discussion on the expected performance.

}, issn = {1548-7105}, doi = {10.1038/s41592-019-0582-9}, author = {Berg, Stuart and Kutra, Dominik and Kroeger, Thorben and Straehle, Christoph N and Kausler, Bernhard X and Haubold, Carsten and Schiegg, Martin and Ales, Janez and Beier, Thorsten and Rudy, Markus and Eren, Kemal and Cervantes, Jaime I and Xu, Buote and Beuttenmueller, Fynn and Wolny, Adrian and Zhang, Chong and Koethe, Ullrich and Hamprecht, Fred A and Kreshuk, Anna} } @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} } @article {47511, title = {Segmenting and Tracking Multiple Dividing Targets Using ilastik.}, journal = {Advances in anatomy, embryology, and cell biology}, volume = {219}, year = {2016}, month = {2016 May 21}, pages = {199-229}, abstract = {

Tracking crowded cells or other targets in biology is often a challenging task due to poor signal-to-noise ratio, mutual occlusion, large displacements, little discernibility, and the ability of cells to divide. We here present an open source implementation of conservation tracking (Schiegg et\ al., IEEE international conference on computer vision (ICCV). IEEE, New York, pp\ 2928-2935, 2013) in the ilastik software framework. This robust tracking-by-assignment algorithm explicitly makes allowance for false positive detections, undersegmentation, and cell division. We give an overview over the underlying algorithm and parameters, and explain the use for a light sheet microscopy sequence of a Drosophila embryo. Equipped with this knowledge, users will be able to track targets of interest in their own data.

}, issn = {0301-5556}, doi = {10.1007/978-3-319-28549-8_8}, author = {Haubold, Carsten and Schiegg, Martin and Kreshuk, Anna and Berg, Stuart and Koethe, Ullrich and Hamprecht, Fred A} } @inbook {47184, title = {Who is talking to whom: Synaptic partner detection in anisotropic volumes of insect brain.}, booktitle = {Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2015}, number = {9349}, year = {2015}, pages = {661-8}, abstract = {

Automated reconstruction of neural connectivity graphs from electron microscopy image stacks is an essential step towards large-scale neural circuit mapping. While significant progress has recently been made in automated segmentation of neurons and detection of synapses, the problem of synaptic partner assignment for polyadic (one-to-many) synapses, prevalent in the\ Drosophila\ brain, remains unsolved. In this contribution, we propose a method which automatically assigns pre- and postsynaptic roles to neurites adjacent to a synaptic site. The method constructs a probabilistic graphical model over potential synaptic partner pairs which includes factors to account for a high rate of one-to-many connections, as well as the possibility of the same neuron to be pre-synaptic in one synapse and post-synaptic in another. The algorithm has been validated on a publicly available stack of ssTEM images of\ Drosophila\ neural tissue and has been shown to reconstruct most of the synaptic relations correctly.

}, doi = {10.1007/978-3-319-24553-9_81}, author = {Kreshuk, Anna and Funke, Jan and Cardona, Albert and Hamprecht, Fred A} } @article {6880, title = {Automated detection of synapses in serial section transmission electron microscopy image stacks.}, journal = {PloS one}, volume = {9}, year = {2014}, month = {2014}, pages = {e87351}, abstract = {We describe a method for fully automated detection of chemical synapses in serial electron microscopy images with highly anisotropic axial and lateral resolution, such as images taken on transmission electron microscopes. Our pipeline starts from classification of the pixels based on 3D pixel features, which is followed by segmentation with an Ising model MRF and another classification step, based on object-level features. Classifiers are learned on sparse user labels; a fully annotated data subvolume is not required for training. The algorithm was validated on a set of 238 synapses in 20 serial 7197{\texttimes}7351 pixel images (4.5{\texttimes}4.5{\texttimes}45 nm resolution) of mouse visual cortex, manually labeled by three independent human annotators and additionally re-verified by an expert neuroscientist. The error rate of the algorithm (12\% false negative, 7\% false positive detections) is better than state-of-the-art, even though, unlike the state-of-the-art method, our algorithm does not require a prior segmentation of the image volume into cells. The software is based on the ilastik learning and segmentation toolkit and the vigra image processing library and is freely available on our website, along with the test data and gold standard annotations (http://www.ilastik.org/synapse-detection/sstem).}, issn = {1932-6203}, doi = {10.1371/journal.pone.0087351}, author = {Kreshuk, Anna and Koethe, Ullrich and Pax, Elizabeth and Bock, Davi D and Hamprecht, Fred A} }