Web-based collaborative neuronal reconstruction with CATMAID

Abstract

Event Abstract Back to Event Web-based collaborative neuronal reconstruction with CATMAID Stephan Gerhard1*, Mark Longair1, Stephan Saalfeld2, Pavel Tomancak2 and Albert Cardona1 1 UZH / ETHZ, Institute of Neuroinformatics, Switzerland 2 Max Planck Institute of Molecular Cell Biology and Genetics, Germany Reconstructing neuronal circuits at such high resolutions that synaptic connections are clearly visible can currently only be done from image data acquired via electron microscopy (EM). These stacks of images enable precise 3D reconstructions of neuronal morphology. While automatic methods for segmenting such images are certainly improving, much annotation and segmentation still needs to be done by human operators carefully examining the images. In addition, the EM data sets that must be dealt with are often many terabytes in size. The requirement for hundreds of annotators to each have a local copy would be prohibitively expensive. To address these requirements, we have extended CATMAID, the Collaborative Annotation Toolkit for Massive Amounts of Image Data,¹ to allow many researchers to trace neurons collaboratively in the same data set. CATMAID is a web-based system, so each annotator only needs a web browser and minimal local storage space requirements. The system already provides an elegant Google Maps-style interface for browsing huge image stacks, collaborative text annotation and a simple interface for asynchronous server-side jobs. We have added two further types of annotation primitive: skeletons and connectors. Skeletons are tree structures suitable for representing the midlines of neuronal arborizations. Connectors link the nodes of skeletons in a many-to-many relationship through a central point, and are suitable for representing polyadic synapses. These annotations are stored in a logical hierarchy, which can be arranged by the researchers in the web interface in order to best represent the structure and biological understanding of the tissue under examination. Every new or changed annotation is immediately reflected in the remote centralized database, so that each researcher always sees up-to-date annotations. Hundreds of annotators can thus concurrently reconstruct the many neurons that make up a circuit. The centralized storage of annotations also allows automatic incremental backups and making the data available via web services. We also have added in-browser 3D visualization of the skeletons, text-tagging of skeleton nodes and connectors, and statistics showing the progress of the tracing. Skeletons can be exported in the standard SWC format for further analysis. While we have tailored the user interface of CATMAID for tracing the midlines of neurons and adding synapses, the annotation primitives of skeletons and connectors are stored in the database as elements of tissue-agnostic subject-predicate-object relations, which means both that the system can be easily adapted to different annotation tasks, and that the data can be made accessible via semantic web technologies. We present, as an example, our progress in tracing a terabyte serial section TEM (Transmission Electron Microscopy) data set from one abdominal segment of the ventral nerve cord of a first instar Drosophila larva. ¹ Saalfeld S, Cardona A, Hartenstein V, Tomancák P (2009) CATMAID: collaborative annotation toolkit for massive amounts of image data. Bioinformatics 25: 1984–1986 Figure 1 Keywords: General neuroinformatics, Neuroimaging Conference: 4th INCF Congress of Neuroinformatics, Boston, United States, 4 Sep - 6 Sep, 2011. Presentation Type: Poster Presentation Topic: General neuroinformatics Citation: Gerhard S, Longair M, Saalfeld S, Tomancak P and Cardona A (2011). Web-based collaborative neuronal reconstruction with CATMAID. Front. Neuroinform. Conference Abstract: 4th INCF Congress of Neuroinformatics. doi: 10.3389/conf.fninf.2011.08.00093 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 17 Oct 2011; Published Online: 19 Oct 2011. * Correspondence: Dr. Stephan Gerhard, UZH / ETHZ, Institute of Neuroinformatics, Zurich, Switzerland, connectome@unidesign.ch Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Stephan Gerhard Mark Longair Stephan Saalfeld Pavel Tomancak Albert Cardona Google Stephan Gerhard Mark Longair Stephan Saalfeld Pavel Tomancak Albert Cardona Google Scholar Stephan Gerhard Mark Longair Stephan Saalfeld Pavel Tomancak Albert Cardona PubMed Stephan Gerhard Mark Longair Stephan Saalfeld Pavel Tomancak Albert Cardona Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.