TissueTag: Jupyter Image Annotator
Abstract
TissueTag consists of two major components: 1) Jupyter-based image annotation tool:Utilising the Bokeh Python library (http://www.bokeh.pydata.org) empowered by Datashader (https://datashader.org/index.html) and holoviews (https://holoviews.org/index.html) for pyramidal image rendering. This tool offers a streamlined annotation solution with subpixel resolution for quick interactive annotation of various image types (e.g., brightfield, fluorescence). TissueTag produces labelled images (e.g., cortex, medulla) and logs all tissue labels, and annotation resolution and colours for reproducibility.Mapping annotations to data: This component facilitates the migration of annotations to spots/cells based on overlap with annotated structures. It also logs the minimum Euclidean distance of each spot/cell to the discrete annotations, offering continuous annotation. This contains spatial neighbourhood information, adding to the x-y coordinates of a given spot or cell, and is foundational for calculating a morphological axis (OrganAxis, see tutorials).
Note: A labeled image is an integer array where each pixel value (0,1,2,…) corresponds to an annotated structure.
Annotator: Enables interactive annotation of predefined anatomical objects via convex shape filling while toggeling between reference and annotation image.
We envision this tool as a foundational starting point as its simplicity and transparent nature allows for many potential enhancements, additions and spinoffs. So contributions and suggestions are highly appreciated!
Curated online tutorial for OrganAxis (https://organ-axis-tutorial.readthedocs.io).
Files
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