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Each Specimen is injected with a viral tracer that labels axons by expressing a fluorescent protein. For each experiment, the injection site is analyzed and assigned a primary injection structure and, if applicable, a list of secondary injection structures.
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In the web application, images from the experiment are visualized in an experimental detail page. All displayed information, images and structural projection values is are also available through the API.
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See the image download page to learn how to download images at different resolution resolutions and regions of interest.
Examples:
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The informatics data processing pipeline produces results that enable the navigation, analysis and visualization of the data. The pipeline consists of the following components:
- an annotated 3-D reference space,
- an alignment module,
- an a projection detection module,
- an a projection gridding module, and
- a structure unionizer module.
The output of the pipeline is quantified projection values at a grid voxel level and at a structure level according to the integrated reference atlas ontology. The grid level data are used downstream to provide a correlative search service and to support visualization of spatial relationships. See the informatics processing whitepaperwhite paper for more details.
3-D Reference Models
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To avoid possible bias introduced by using a single specimen as a registration target, the Nissl-based 3-D reference volume was not directly used. Instead, a large number of brains were mapped in advanced and averaged to form the registration target. This averaged template maybe may be updated periodically to include more brain specimens and is available for download.
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3-D annotation volumes were updated in the June 2013 release to reflect changes in the atlas drawings and ontology. Also note that the volumes are now in a 32-bit format to large structure identifiers. |
Four Five volumetric data files are available for download:
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The aim of image alignment is to establish a mapping from each SectionImage to the 3-D reference space. The module reconstructs a 3-D Specimen volume from its constituent SectionImages and registers the volume to the 3-D reference model by maximizing mutual information between the red channel of the experimental data and the average template.
Once registration is achieved, information from the 3-D reference model can be transferred to the reconstructed Specimen and vice versa. The resulting transform information is stored in the database. Each SectionImage has an Alignment2d object that represents the 2-D affine transform between a an image pixel position to and a location in the Specimen volume. Each SectionDataSet has an Alignment3d object that represents the 3-D affine transform between a location in the Specimen volume and a point in the 3-D reference model. Spatial correspondence between any two SectionDataSets from different Specimens can be established by composing these transforms.
For convenience, a set of "Image Sync" API methods is available to find corresponding position positions between SectionDataSets, the 3-D reference model and structures. Note that all locations on SectionImages are reported in pixel coordinates and all locations in 3-D ReferenceSpaces are reported in microns. These methods are used by the Web application to provide the image synchronization feature in the multiple image viewer (see Figure).
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For every Projection image, a gray scale grayscale mask is generated that identifies pixels corresponding to labeled axon trajectories. The segmentation algorithm is based on image edge/line detection and morphological filtering.
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