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39 Janelia Publications

Showing 31-39 of 39 results
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    12/23/16 | Image-based correction of continuous and discontinuous non-planar axial distortion in serial section microscopy.
    Hanslovsky P, Bogovic JA, Saalfeld S
    Bioinformatics (Oxford, England). 2016 Dec 23:. doi: 10.1093/bioinformatics/btw794

    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.

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    Saalfeld LabFly Functional Connectome
    06/15/16 | Robust registration of calcium images by learned contrast synthesis.
    Bogovic JA, Hanslovsky P, Wong AM, Saalfeld S
    IEEE 13th International Symposium on Biomedical Imaging: From Nano to Macro. 2016 Jun 15:. doi: 10.1109/ISBI.2016.7493463

    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.

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    03/18/16 | Quantitative neuroanatomy for connectomics in Drosophila.
    Schneider-Mizell CM, Gerhard S, Longair M, Kazimiers T, Li F, Zwart M, Champion A, Midgley F, Fetter RD, Saalfeld S, Cardona A
    eLife. 2016 Mar 18:e12059. doi: 10.7554/eLife.12059

    Neuronal circuit mapping using electron microscopy demands laborious proofreading or reconciliation of multiple independent reconstructions. Here, we describe new methods to apply quantitative arbor and network context to iteratively proofread and reconstruct circuits and create anatomically enriched wiring diagrams. We measured the morphological underpinnings of connectivity in new and existing reconstructions of Drosophila sensorimotor (larva) and visual (adult) systems. Synaptic inputs were preferentially located on numerous small, microtubule-free 'twigs' which branch off a single microtubule-containing 'backbone'. Omission of individual twigs accounted for 96% of errors. However, the synapses of highly connected neurons were distributed across multiple twigs. Thus, the robustness of a strong connection to detailed twig anatomy was associated with robustness to reconstruction error. By comparing iterative reconstruction to the consensus of multiple reconstructions, we show that our method overcomes the need for redundant effort through the discovery and application of relationships between cellular neuroanatomy and synaptic connectivity.

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    09/23/15 | Automated cerebellar lobule segmentation with application to cerebellar structural analysis in cerebellar disease.
    Yang Z, Ye C, Bogovic JA, Carass A, Jedynak BM, Ying SH, Prince JL
    NeuroImage. 2015 Sep 23;127:435-44. doi: 10.1016/j.neuroimage.2015.09.032

    The cerebellum plays an important role in both motor control and cognitive function. Cerebellar function is topographically organized and diseases that affect specific parts of the cerebellum are associated with specific patterns of symptoms. Accordingly, delineation and quantification of cerebellar sub-regions from magnetic resonance images are important in the study of cerebellar atrophy and associated functional losses. This paper describes an automated cerebellar lobule segmentation method based on a graph cut segmentation framework. Results from multi-atlas labeling and tissue classification contribute to the region terms in the graph cut energy function and boundary classification contributes to the boundary term in the energy function. A cerebellar parcellation is achieved by minimizing the energy function using the α-expansion technique. The proposed method was evaluated using a leave-one-out cross-validation on 15 subjects including both healthy controls and patients with cerebellar diseases. Based on reported Dice coefficients, the proposed method outperforms two state-of-the-art methods. The proposed method was then applied to 2(j) 77 subjects to study the region-specific cerebellar structural differences in three spinocerebellar ataxia (SCA) genetic subtypes. Quantitative analysis of the lobule volumes show distinct patterns of volume changes associated with different SCA subtypes consistent with known patterns of atrophy in these genetic subtypes.

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    07/01/15 | Post-acquisition image based compensation for thickness variation in microscopy section series.
    Hanslovsky P, Bogovic J, Saalfeld S
    IEEE 12th International Symposium on Biomedical Imaging (ISBI). 2015 Jul 01:. doi: 10.1109/ISBI.2015.7163922

    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.

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    Saalfeld LabSinger Lab
    05/28/15 | BigDataViewer: visualization and processing for large image data sets.
    Pietzsch T, Saalfeld S, Preibisch S, Tomancak P
    Nature Methods. 2015 May 28;12(6):481-3. doi: 10.1038/nmeth.3392
    11/25/14 | Post-acquisition image based compensation for thickness variation in microscopy section series.
    Hanslovsky P, Bogovic JA, Saalfeld S
    IEEE International Symposium on Biomedical Imaging. 2014 Nov 25:507-11

    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.

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    Cardona LabSaalfeld LabFetter Lab
    07/01/12 | Elastic volume reconstruction from series of ultra-thin microscopy sections.
    Saalfeld S, Fetter RD, Cardona A, Tomancak P
    Nature Methods. 2012 Jul;9(7):717-20. doi: 10.1038/nmeth.2072

    Anatomy of large biological specimens is often reconstructed from serially sectioned volumes imaged by high-resolution microscopy. We developed a method to reassemble a continuous volume from such large section series that explicitly minimizes artificial deformation by applying a global elastic constraint. We demonstrate our method on a series of transmission electron microscopy sections covering the entire 558-cell Caenorhabditis elegans embryo and a segment of the Drosophila melanogaster larval ventral nerve cord.

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    Cardona LabSaalfeld Lab
    06/02/10 | Identifying neuronal lineages of Drosophila by sequence analysis of axon tracts.
    Cardona A, Saalfeld S, Arganda I, Pereanu W, Schindelin J, Hartenstein V
    The Journal of Neuroscience. 2010 Jun 2;30(22):7538-53. doi: 10.1523/JNEUROSCI.0186-10.2010

    The Drosophila brain is formed by an invariant set of lineages, each of which is derived from a unique neural stem cell (neuroblast) and forms a genetic and structural unit of the brain. The task of reconstructing brain circuitry at the level of individual neurons can be made significantly easier by assigning neurons to their respective lineages. In this article we address the automation of neuron and lineage identification. We focused on the Drosophila brain lineages at the larval stage when they form easily recognizable secondary axon tracts (SATs) that were previously partially characterized. We now generated an annotated digital database containing all lineage tracts reconstructed from five registered wild-type brains, at higher resolution and including some that were previously not characterized. We developed a method for SAT structural comparisons based on a dynamic programming approach akin to nucleotide sequence alignment and a machine learning classifier trained on the annotated database of reference SATs. We quantified the stereotypy of SATs by measuring the residual variability of aligned wild-type SATs. Next, we used our method for the identification of SATs within wild-type larval brains, and found it highly accurate (93-99%). The method proved highly robust for the identification of lineages in mutant brains and in brains that differed in developmental time or labeling. We describe for the first time an algorithm that quantifies neuronal projection stereotypy in the Drosophila brain and use the algorithm for automatic neuron and lineage recognition.

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