Main Menu (Mobile)- Block

Main Menu - Block

custom | custom

Search Results

filters_region_cap | custom


facetapi-Q2b17qCsTdECvJIqZJgYMaGsr8vANl1n | block
facetapi-W9JlIB1X0bjs93n1Alu3wHJQTTgDCBGe | block
facetapi-61yz1V0li8B1bixrCWxdAe2aYiEXdhd0 | block
facetapi-PV5lg7xuz68EAY8eakJzrcmwtdGEnxR0 | block
facetapi-aK0bSsPXQOqhYQEgonL2xGNrv4SPvFLb | block

Tool Types

general_search_page-panel_pane_1 | views_panes

5 Janelia Publications

Showing 1-5 of 5 results
Your Criteria:
    09/26/18 | Synaptic cleft segmentation in non-isotropic volume electron microscopy of the complete Drosophila brain.
    Heinrich L, Funke J, Pape C, Nunez-Iglesias J, Saalfeld S
    Medical Image Computing and Computer Assisted Intervention – MICCAI 2018. 2018 Sep 26:317-25. doi: 10.1007/978-3-030-00934-2_36

    Neural circuit reconstruction at single synapse resolution is increasingly recognized as crucially important to decipher the function of biological nervous systems. Volume electron microscopy in serial transmission or scanning mode has been demonstrated to provide the necessary resolution to segment or trace all neurites and to annotate all synaptic connections. 
    Automatic annotation of synaptic connections has been done successfully in near isotropic electron microscopy of vertebrate model organisms. Results on non-isotropic data in insect models, however, are not yet on par with human annotation. 
    We designed a new 3D-U-Net architecture to optimally represent isotropic fields of view in non-isotropic data. We used regression on a signed distance transform of manually annotated synaptic clefts of the CREMI challenge dataset to train this model and observed significant improvement over the state of the art. 
    We developed open source software for optimized parallel prediction on very large volumetric datasets and applied our model to predict synaptic clefts in a 50 tera-voxels dataset of the complete Drosophila brain. Our model generalizes well to areas far away from where training data was available.

    View Publication Page
    07/25/18 | An unbiased template of the Drosophila brain and ventral nerve cord.
    Bogovic JA, Otsuna H, Heinrich L, Ito M, Jeter J, Meissner GW, Nern A, Colonell J, Malkesman O, Saalfeld S
    bioRxiv. 2018 Jul 25:. doi: 10.1101/376384

    The fruit fly Drosophila melanogaster is an important model organism for neuroscience with a wide array of genetic tools that enable the mapping of individuals neurons and neural subtypes. Brain templates are essential for comparative biological studies because they enable analyzing many individuals in a common reference space. Several central brain templates exist for Drosophila, but every one is either biased, uses sub-optimal tissue preparation, is imaged at low resolution, or does not account for artifacts. No publicly available Drosophila ventral nerve cord template currently exists. In this work, we created high-resolution templates of the Drosophila brain and ventral nerve cord using the best-available technologies for imaging, artifact correction, stitching, and template construction using groupwise registration. We evaluated our central brain template against the four most competitive, publicly available brain templates and demonstrate that ours enables more accurate registration with fewer local deformations in shorter time.

    View Publication Page
    07/12/18 | A complete electron microscopy volume of the brain of adult Drosophila melanogaster.
    Zheng 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
    Cell. 2018 Jul 12;174(3):730-43. doi: 10.1016/j.cell.2018.06.019

    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..

    View Publication Page
    05/24/18 | The candidate multi-cut for cell segmentation.
    Funke J, Zhang C, Pietzsch T, Gonzalez Ballester MA, Saalfeld S
    2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018). 2017 Jul 04:. doi: 10.1109/ISBI.2018.8363658

    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.

    View Publication Page
    04/26/18 | Joint deformable registration of large EM image volumes: a matrix solver approach.
    Khairy K, Denisov G, Saalfeld S
    arXiv. 2018 Apr 26:

    Large electron microscopy image datasets for connectomics are typically composed of thousands to millions of partially overlapping two-dimensional images (tiles), which must be registered into a coherent volume prior to further analysis. A common registration strategy is to find matching features between neighboring and overlapping image pairs, followed by a numerical estimation of optimal image deformation using a so-called solver program. 
    Existing solvers are inadequate for large data volumes, and inefficient for small-scale image registration. 
    In this work, an efficient and accurate matrix-based solver method is presented. A linear system is constructed that combines minimization of feature-pair square distances with explicit constraints in a regularization term. In absence of reliable priors for regularization, we show how to construct a rigid-model approximation to use as prior. The linear system is solved using available computer programs, whose performance on typical registration tasks we briefly compare, and to which future scale-up is delegated. Our method is applied to the joint alignment of 2.67 million images, with more than 200 million point-pairs and has been used for successfully aligning the first full adult fruit fly brain.

    View Publication Page