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16 Janelia Publications

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    09/09/17 | A deep structured learning approach towards automating connectome reconstruction from 3D electron micrographs.
    Funke J, Tschopp FD, Grisaitis W, Singh C, Saalfeld S, Turaga SC
    arXiv. 2017 Sep 09:

    We present a deep learning method for neuron segmentation from 3D electron microscopy (EM), which improves significantly upon state of the art in terms of accuracy and scalability. Our method consists of a fully 3D extension of the U-NET architecture, trained to predict affinity graphs on voxels, followed by a simple and efficient iterative region agglomeration. We train the U-NET using a structured loss function based on MALIS that encourages topological correctness. The resulting affinity predictions are accurate enough that we obtain state-of-the-art results by a simple new learning-free percentile-based iterative agglomeration algorithm. We demonstrate the accuracy of our method on three different and diverse EM datasets where we significantly improve over the current state of the art. We also show for the first time that a common 3D segmentation strategy can be applied to both well-aligned nearly isotropic block-face EM data, and poorly aligned anisotropic serial sectioned EM data. The runtime of our method scales with O(n) in the size of the volume and is thus ready to be applied to very large datasets.

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    07/06/17 | Building bridges between cellular and molecular structural biology.
    Patwardhan A, Brandt R, Butcher SJ, Collinson L, Gault D, Grünewald K, Hecksel C, Huiskonen JT, Iudin A, Jones ML, Korir PK, Koster AJ, Lagerstedt I, Lawson CL, Mastronarde D, McCormick M, Parkinson H, Rosenthal PB, Saalfeld S, Saibil HR, Sarntivijai S, Solanes Valero I, Subramaniam S, Swedlow JR, Tudose I, Winn M, Kleywegt GJ
    eLife. 2017 Jul 06;6:. doi: 10.7554/eLife.25835

    The integration of cellular and molecular structural data is key to understanding the function of macromolecular assemblies and complexes in their in vivo context. Here we report on the outcomes of a workshop that discussed how to integrate structural data from a range of public archives. The workshop identified two main priorities: the development of tools and file formats to support segmentation (that is, the decomposition of a three-dimensional volume into regions that can be associated with defined objects), and the development of tools to support the annotation of biological structures.

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    07/04/17 | The candidate multi-cut for cell segmentation.
    Funke J, Zhang C, Pietzsch T, Saalfeld S
    arXiv. 2017 Jul 04:1707.00907

    Two successful approaches for the segmentation of biomedical images are (1) the selection of segment candidates from a merge-tree, and (2) the clustering of small superpixels by solving a Multi-Cut problem. In this paper, we introduce a model that unifies both approaches. Our model, the Candidate Multi-Cut (CMC), allows joint selection and clustering of segment candidates from a merge-tree. This way, we overcome the respective limitations of the individual methods: (1) the space of possible segmentations is not constrained to candidates of a merge-tree, and (2) the decision for clustering can be made on candidates larger than superpixels, using features over larger contexts. We solve the optimization problem of selecting and clustering of candidates using an integer linear program. On datasets of 2D light microscopy of cell populations and 3D electron microscopy of neurons, we show that our method generalizes well and generates more accurate segmentations than merge-tree or Multi-Cut methods alone.

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    06/13/17 | A complete electron microscopy volume of the brain of adult Drosophila melanogaster.
    Yang Z, Lauritzen JS, Perlman E, Robinson CG, Nichols M, Milkie DE, Torrens O, Price J, Fisher CB, Sharifi N, Calle-Schuler SA, Kmecova L, Ali IJ, Karsh B, Trautman ET, Bogovic JA, Hanslovsky P, Jefferis GS, Kazhdan M, Khairy K
    bioRxiv. 2017 Jun 13:. doi: 10.1101/140905

    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. We validated the dataset by tracing brain-spanning circuitry involving the mushroom body (MB), intensively studied for its role in learning. Here we describe the complete set of olfactory inputs to the MB; find a new cell type providing driving input to Kenyon cells (the intrinsic MB neurons); identify neurons postsynaptic to Kenyon cell dendrites; and find that axonal arbors providing input to the MB calyx are more tightly clustered than previously indicated by light-level data. This freely available EM dataset will significantly accelerate Drosophila neuroscience.

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    06/09/17 | Deep learning for isotropic super-resolution from non-isotropic 3D electron microscopy.
    Heinrich L, Bogovic JA, Saalfeld S
    arXiv. 2017 Jun 09:arXiv:1706.03142

    he most sophisticated existing methods to generate 3D isotropic super-resolution (SR) from non-isotropic electron microscopy (EM) are based on learned dictionaries. Unfortunately, none of the existing methods generate practically satisfying results. For 2D natural images, recently developed super-resolution methods that use deep learning have been shown to significantly outperform the previous state of the art. 
    We have adapted one of the most successful architectures (FSRCNN) for 3D super-resolution, and compared its performance to a 3D U-Net architecture that has not been used previously to generate super-resolution. 
    We trained both architectures on artificially downscaled isotropic ground truth from focused ion beam milling scanning EM (FIB-SEM) and tested the performance for various hyperparameter settings. 
    Our results indicate that both architectures can successfully generate 3D isotropic super-resolution from non-isotropic EM, with the U-Net performing consistently better. We propose several promising directions for practical application.

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    05/18/17 | Whole-brain serial-section electron microscopy in larval zebrafish.
    Hildebrand DG, Cicconet M, Torres RM, Choi W, Quan TM, Moon J, Wetzel AW, Scott Champion A, Graham BJ, Randlett O, Plummer GS, Portugues R, Bianco IH, Saalfeld S, Baden AD, Lillaney K, Burns R, Vogelstein JT, Schier AF, Lee WA, Jeong W, Lichtman JW, Engert F
    Nature. 2017 May 18;545(7654):345-349. doi: 10.1038/nature22356

    High-resolution serial-section electron microscopy (ssEM) makes it possible to investigate the dense meshwork of axons, dendrites, and synapses that form neuronal circuits. However, the imaging scale required to comprehensively reconstruct these structures is more than ten orders of magnitude smaller than the spatial extents occupied by networks of interconnected neurons, some of which span nearly the entire brain. Difficulties in generating and handling data for large volumes at nanoscale resolution have thus restricted vertebrate studies to fragments of circuits. These efforts were recently transformed by advances in computing, sample handling, and imaging techniques, but high-resolution examination of entire brains remains a challenge. Here, we present ssEM data for the complete brain of a larval zebrafish (Danio rerio) at 5.5 days post-fertilization. Our approach utilizes multiple rounds of targeted imaging at different scales to reduce acquisition time and data management requirements. The resulting dataset can be analysed to reconstruct neuronal processes, permitting us to survey all myelinated axons (the projectome). These reconstructions enable precise investigations of neuronal morphology, which reveal remarkable bilateral symmetry in myelinated reticulospinal and lateral line afferent axons. We further set the stage for whole-brain structure-function comparisons by co-registering functional reference atlases and in vivo two-photon fluorescence microscopy data from the same specimen. All obtained images and reconstructions are provided as an open-access resource.

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    04/05/17 | PreMosa: Extracting 2D surfaces from 3D microscopy mosaics.
    Blasse C, Saalfeld S, Etournay R, Sagner A, Eaton S, Myers EW
    Bioinformatics (Oxford, England). 2017 Apr 05:. doi: 10.1093/bioinformatics/btx195

    Motivation: A significant focus of biological research is to understand the development, organization and function of tissues. A particularly productive area of study is on single layer epithelial tissues in which the adherence junctions of cells form a 2D manifold that is fluorescently labeled. Given the size of the tissue, a microscope must collect a mosaic of overlapping 3D stacks encompassing the stained surface. Downstream interpretation is greatly simplified by preprocessing such a dataset as follows: (a) extracting and mapping the stained manifold in each stack into a single 2D projection plane, (b) correcting uneven illumination artifacts, (c) stitching the mosaic planes into a single, large 2D image, and (d) adjusting the contrast.

    Results: We have developed PreMosa, an efficient, fully automatic pipeline to perform the four preprocessing tasks above resulting in a single 2D image of the stained manifold across which contrast is optimized and illumination is even. Notable features are as follows. First, the 2D projection step employs a specially developed algorithm that actually finds the manifold in the stack based on maximizing contrast, intensity and smoothness. Second, the projection step comes first, implying all subsequent tasks are more rapidly solved in 2D. And last, the mosaic melding employs an algorithm that globally adjusts contrasts amongst the 2D tiles so as to produce a seamless, high-contrast image. We conclude with an evaluation using ground-truth datasets and present results on datasets from Drosophila melanogaster wings and Schmidtae mediterranea ciliary components.

    Availability: PreMosa is available under https://cblasse.github.io/premosa.

    Contact: blasse@mpi-cbg.de, myers@mpi-cbg.de.

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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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