  We address the problem of detecting tree rings in microscopy images of shrub
cross sections. This can be regarded as a special case of the instance
segmentation task with several unique challenges such as the concentric
circular ring shape of the objects and high precision requirements that result
in inadequate performance of existing methods. We propose a new iterative
method which we term Iterative Next Boundary Detection (INBD). It intuitively
models the natural growth direction, starting from the center of the shrub
cross section and detecting the next ring boundary in each iteration step. In
our experiments, INBD shows superior performance to generic instance
segmentation methods and is the only one with a built-in notion of
chronological order. Our dataset and source code are available at
http://github.com/alexander-g/INBD.

Computer Vision and Pattern Recognition