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4 Janelia Publications

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    09/04/17 | Deep learning for isotropic super-resolution from non-isotropic 3D electron microscopy.
    Heinrich L, Bogovic JA, Saalfeld S
    International Conference on Medical Image Computing and Computer-Assisted Intervention - MICCAI 2017. MICCAI 2017. Lecture Notes in Computer Science. 2017 Sept 4;10434:arXiv:1706.03142. doi: 10.1007/978-3-319-66185-8_16

    The 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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    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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    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;33(16):2563-9. 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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