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.

翻译：我们致力于解决灌木横截面显微图像中树木年轮检测的问题。该问题可视为实例分割任务的一个特殊情形，面临若干独特挑战，例如目标呈同心圆环形状，且对精度要求极高，导致现有方法性能不足。我们提出一种新的迭代方法，命名为迭代下一边界检测（INBD）。该方法直观地模拟自然生长方向，从灌木横截面中心出发，在每次迭代步骤中检测下一个环状边界。实验表明，INBD在性能上优于通用实例分割方法，且是唯一内置时间顺序概念的方法。我们的数据集和源代码可在http://github.com/alexander-g/INBD获取。