Web-based collaborative neuronal reconstruction with CATMAID
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1
UZH / ETHZ, Institute of Neuroinformatics, Switzerland
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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
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
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Received:
17 Oct 2011;
Published Online:
19 Oct 2011.
*
Correspondence:
Dr. Stephan Gerhard, UZH / ETHZ, Institute of Neuroinformatics, Zurich, Switzerland, connectome@unidesign.ch