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6 Publications

Showing 1-6 of 6 results
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    Cardona Lab
    07/01/12 | Current challenges in open-source bioimage informatics.
    Cardona A, Tomancak P
    Nature Methods. 2012 Jul;9(7):661-5. doi: 10.1038/nmeth.2082

    We discuss the advantages and challenges of the open-source strategy in biological image analysis and argue that its full impact will not be realized without better support and recognition of software engineers’ contributions to the biological sciences and more support of this development model from funders and institutions.

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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
    07/01/12 | Fiji: an open-source platform for biological-image analysis.
    Schindelin J, Arganda-Carreras I, Frise E, Kaynig V, Longair M, Pietzsch T, Preibisch S, Rueden C, Saalfeld S, Schmid B, Tinevez J, White DJ, Hartenstein V, Eliceiri K, Tomancak P, Cardona A
    Nature Methods. 2012 Jul;9(7):676-82. doi: 10.1038/nmeth.2019

    Fiji is a distribution of the popular open-source software ImageJ focused on biological-image analysis. Fiji uses modern software engineering practices to combine powerful software libraries with a broad range of scripting languages to enable rapid prototyping of image-processing algorithms. Fiji facilitates the transformation of new algorithms into ImageJ plugins that can be shared with end users through an integrated update system. We propose Fiji as a platform for productive collaboration between computer science and biology research communities.

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    Saalfeld LabCardona Lab
    06/19/12 | TrakEM2 software for neural circuit reconstruction.
    Cardona A, Saalfeld S, Schindelin J, Arganda-Carreras I, Preibisch S, Longair M, Tomancak P, Hartenstein V, Douglas RJ
    PLoS One. 2012;7(6):e38011. doi: 10.1371/journal.pone.0038011

    A key challenge in neuroscience is the expeditious reconstruction of neuronal circuits. For model systems such as Drosophila and C. elegans, the limiting step is no longer the acquisition of imagery but the extraction of the circuit from images. For this purpose, we designed a software application, TrakEM2, that addresses the systematic reconstruction of neuronal circuits from large electron microscopical and optical image volumes. We address the challenges of image volume composition from individual, deformed images; of the reconstruction of neuronal arbors and annotation of synapses with fast manual and semi-automatic methods; and the management of large collections of both images and annotations. The output is a neural circuit of 3d arbors and synapses, encoded in NeuroML and other formats, ready for analysis.

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    Cardona Lab
    01/01/12 | Efficient automatic 3D-reconstruction of branching neurons from EM data.
    Funke J, Andres B, Hamprecht F, Cardona A, Cook M
    Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on. 2012:

    We present an approach for the automatic reconstruction of neurons from 3D stacks of electron microscopy sections. The core of our system is a set of possible assignments, each of which proposes with some cost a link between neuron regions in consecutive sections. These can model the continuation, branching, and end of neurons. The costs are trainable on positive assignment samples. An optimal and consistent set of assignments is found for the whole volume at once by solving an integer linear program. This set of assignments determines both the segmentation into neuron regions and the correspondence between such regions in neighboring slices. For each picked assignment, a confidence value helps to prioritize decisions to be reviewed by a human expert. We evaluate the performance of our method on an annotated volume of neural tissue and compare to the current state of the art [26]. Our method is superior in accuracy and can be trained using a small number of samples. The observed inference times are linear with about 2 milliseconds per neuron and section.

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    Cardona Lab
    01/01/12 | TrakEM2 software for neural circuit reconstruction.
    Cardona A, Saalfeld S, Schindelin J, Arganda-Carreras I, Preibisch S, Longair M, Tomancak P, Hartenstein V, Douglas RJ
    PLoS One. 2012;7:e38011. doi: 10.1371/journal.pone.0038011

    A key challenge in neuroscience is the expeditious reconstruction of neuronal circuits. For model systems such as Drosophila and C. elegans, the limiting step is no longer the acquisition of imagery but the extraction of the circuit from images. For this purpose, we designed a software application, TrakEM2, that addresses the systematic reconstruction of neuronal circuits from large electron microscopical and optical image volumes. We address the challenges of image volume composition from individual, deformed images; of the reconstruction of neuronal arbors and annotation of synapses with fast manual and semi-automatic methods; and the management of large collections of both images and annotations. The output is a neural circuit of 3d arbors and synapses, encoded in NeuroML and other formats, ready for analysis.

    View Publication Page