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      DP2: Distributed 3D image segmentation using micro-labor workforce

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      * , ,
      Bioinformatics
      Oxford University Press

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          Abstract

          Summary: This application note describes a new scalable semi-automatic approach, the Dual Point Decision Process, for segmentation of 3D structures contained in 3D microscopy. The segmentation problem is distributed to many individual workers such that each receives only simple questions regarding whether two points in an image are placed on the same object. A large pool of micro-labor workers available through Amazon’s Mechanical Turk system provides the labor in a scalable manner.

          Availability and implementation: Python-based code for non-commercial use and test data are available in the source archive at https://sites.google.com/site/imagecrowdseg/.

          Contact: rgiuly@ 123456ucsd.edu

          Supplementary information: Supplementary data are available at Bioinformatics online.

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          Most cited references14

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          Enhancing Serial Block-Face Scanning Electron Microscopy to Enable High Resolution 3-D Nanohistology of Cells and Tissues

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            Engineering and algorithm design for an image processing Api: a technical report on ITK--the Insight Toolkit.

            We present the detailed planning and execution of the Insight Toolkit (ITK), an application programmers interface (API) for the segmentation and registration of medical image data. This public resource has been developed through the NLM Visible Human Project, and is in beta test as an open-source software offering under cost-free licensing. The toolkit concentrates on 3D medical data segmentation and registration algorithms, multimodal and multiresolution capabilities, and portable platform independent support for Windows, Linux/Unix systems. This toolkit was built using current practices in software engineering. Specifically, we embraced the concept of generic programming during the development of these tools, working extensively with C++ templates and the freedom and flexibility they allow. Software development tools for distributed consortium-based code development have been created and are also publicly available. We discuss our assumptions, design decisions, and some lessons learned.
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              Semi-automated reconstruction of neural circuits using electron microscopy.

              Reconstructing neuronal circuits at the level of synapses is a central problem in neuroscience, and the focus of the nascent field of connectomics. Previously used to reconstruct the C. elegans wiring diagram, serial-section transmission electron microscopy (ssTEM) is a proven technique for the task. However, to reconstruct more complex circuits, ssTEM will require the automation of image processing. We review progress in the processing of electron microscopy images and, in particular, a semi-automated reconstruction pipeline deployed at Janelia Farm. Drosophila circuits underlying identified behaviors are being reconstructed in the pipeline with the goal of generating a complete Drosophila connectome. Copyright © 2010. Published by Elsevier Ltd.
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                Author and article information

                Journal
                Bioinformatics
                Bioinformatics
                bioinformatics
                bioinfo
                Bioinformatics
                Oxford University Press
                1367-4803
                1367-4811
                15 May 2013
                10 April 2013
                10 April 2013
                : 29
                : 10
                : 1359-1360
                Affiliations
                National Center for Microscopy and Imaging Research, Center for Research in Biological Systems, Department of Neurosciences, University of California, San Diego, La Jolla, CA 92093, USA
                Author notes
                *To whom correspondence should be addressed.

                Associate Editor: Jonathan Wren

                Article
                btt154
                10.1093/bioinformatics/btt154
                3654713
                23574738
                f1fbe095-5fd9-40f0-abff-b186ae5e0673
                © The Author 2013. Published by Oxford University Press.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/3.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 15 October 2012
                : 26 March 2013
                : 26 March 2013
                Page count
                Pages: 2
                Categories
                Applications Notes
                Bioimage Informatics

                Bioinformatics & Computational biology
                Bioinformatics & Computational biology

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