In typical use scenarios, VANO is started (i) on just a raw image with no objects (ii) with a segmentation mask produced by a computation (e.g. Moreover, objects can be created, deleted, split, merged or redefined by commands that directly manipulate the segmentation mask. For a given object, one can label it and set any number of user-defined attributes either in the spreadsheet or from the 3D views. VANO is a cross-platform 3D annotator that provides a spreadsheet of all 3D image objects that is linked to both 3D view of the raw image and a segmentation ‘mask’ that specifies the voxels that belong to each object ( Fig. This tool has and continues to play a critical role in our recent work in building 3D digital atlases of the L1 stage of Caenorhabditis elegans, and the late embryo ventral nerve cord and adult brain of Drosophila melanogaster. VANO, short for volume-object annotation system, was developed specifically to allow one to produce annotations manually and to correct or refine the output of good, but not perfect, automated annotation methods. Critical to their development is the availability of a corpus of curated training data, and since none of these methods is perfect, their application in the field is benefited by having the ability to manually curate the results they produce. Many methods have been developed for determining annotations computationally such as categorizing gene expression patterns (e.g Zhou and Peng, 2007) or predicting cell identities (e.g. Image content annotation is a basic problem in the analysis of 3D high-resolution cellular and molecular images (Peng, 2008).
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