@article {48620, title = {A complete electron microscopy volume of the brain of adult Drosophila melanogaster.}, journal = {Cell}, volume = {174}, year = {2018}, month = {2018 Jul 12}, pages = {730-43}, abstract = {

Drosophila melanogaster has a rich repertoire of innate and learned behaviors. Its 100,000-neuron brain is a large but tractable target for comprehensive neural circuit mapping. Only electron microscopy (EM) enables complete, unbiased mapping of synaptic connectivity; however, the fly brain is too large for conventional EM. We developed a custom high-throughput EM platform and imaged the entire brain of an adult female fly at synaptic resolution. To validate the dataset, we traced brain-spanning circuitry involving the mushroom body (MB), which has been extensively studied for its role in learning. All inputs to Kenyon cells (KCs), the intrinsic neurons of the MB, were mapped, revealing a previously unknown cell type, postsynaptic partners of KC dendrites, and unexpected clustering of olfactory projection neurons. These reconstructions show that this freely available EM volume supports mapping of brain-spanning circuits, which will significantly accelerate Drosophila neuroscience..

}, doi = {10.1016/j.cell.2018.06.019}, author = {Zheng, Zhihao and Lauritzen, J Scott and Perlman, Eric and Robinson, Camenzind G and Nichols, Matthew and Milkie, Daniel E and Torrens, Omar and Price, John and Fisher, Corey B and Sharifi, Nadiya and Calle-Schuler, Steven A and Kmecova, Lucia and Ali, Iqbal J and Karsh, Bill and Trautman, Eric T and Bogovic, John A and Hanslovsky, Philipp and Jefferis, Gregory S X E and Kazhdan, Michael and Khairy, Khaled} } @article {48296, title = {Image-based correction of continuous and discontinuous non-planar axial distortion in serial section microscopy.}, journal = {Bioinformatics (Oxford, England)}, year = {2016}, month = {2016 Dec 23}, abstract = {

MOTIVATION: Serial section microscopy is an established method for detailed anatomy reconstruction of biological specimen. During the last decade, high resolution electron microscopy (EM) of serial sections has become the de-facto standard for reconstruction of neural connectivity at ever increasing scales (EM connectomics). In serial section microscopy, the axial dimension of the volume is sampled by physically removing thin sections from the embedded specimen and subsequently imaging either the block-face or the section series. This process has limited precision leading to inhomogeneous non-planar sampling of the axial dimension of the volume which, in turn, results in distorted image volumes. This includes that section series may be collected and imaged in unknown order.

RESULTS: We developed methods to identify and correct these distortions through image-based signal analysis without any additional physical apparatus or measurements. We demonstrate the efficacy of our methods in proof of principle experiments and application to real world problems.

AVAILABILITY AND IMPLEMENTATION: We made our work available as libraries for the ImageJ distribution Fiji and for deployment in a high performance parallel computing environment. Our sources are open and available at http://github.com/saalfeldlab/section-sort, http://github.com/saalfeldlab/z-spacing and http://github.com/saalfeldlab/z-spacing-spark CONTACT: : saalfelds@janelia.hhmi.orgSupplementary information: Supplementary data are available at Bioinformatics online.

}, issn = {1367-4811}, doi = {10.1093/bioinformatics/btw794}, author = {Hanslovsky, Philipp and Bogovic, John A and Saalfeld, Stephan} } @conference {47735, title = {Robust registration of calcium images by learned contrast synthesis.}, booktitle = {IEEE 13th International Symposium on Biomedical Imaging: From Nano to Macro}, year = {2016}, month = {2016 Jun 15}, address = {Clarion Congress Hotel in Prague Prague; Czech Republic}, abstract = {

Multi-modal image registration is a challenging task that is vital to fuse complementary signals for subsequent analyses. Despite much research into cost functions addressing this challenge, there exist cases in which these are ineffective. In this work, we show that (1) this is true for the registration of in-vivo Drosophila brain volumes visualizing genetically encoded calcium indicators to an nc82 atlas and (2) that machine learning based contrast synthesis can yield improvements. More specifically, the number of subjects for which the registration outright failed was greatly reduced (from 40\% to 15\%) by using a synthesized image.

}, isbn = {978-147992350-2}, doi = {10.1109/ISBI.2016.7493463}, author = {Bogovic, John A and Hanslovsky, Philipp and Wong, Allen M and Saalfeld, Stephan} } @conference {47129, title = {Post-acquisition image based compensation for thickness variation in microscopy section series.}, booktitle = {IEEE 12th International Symposium on Biomedical Imaging (ISBI)}, year = {2015}, month = {2015 Jul 01}, abstract = {

Serial section Microscopy is an established method for volumetric anatomy reconstruction. Section series imaged with Electron Microscopy are currently vital for the reconstruction of the synaptic connectivity of entire animal brains such as that of Drosophila melanogaster. The process of removing ultrathin layers from a solid block containing the specimen, however, is a fragile procedure and has limited precision with respect to section thickness. We have developed a method to estimate the relative z-position of each individual section as a function of signal change across the section series. First experiments show promising results on both serial section Transmission Electron Microscopy (ssTEM) data and Focused Ion Beam Scanning Electron Microscopy (FIB-SEM) series. We made our solution available as Open Source plugins for the TrakEM2 software and the ImageJ distribution Fiji.

}, doi = {10.1109/ISBI.2015.7163922}, author = {Hanslovsky, Philipp and Bogovic, John and Saalfeld, Stephan} }