Binary Image Skeletonization Using 2-Stage U-Net
This work addresses skeletonization for applications like anatomical research and activity detection, but it is incremental as it builds on existing U-Net architectures with a novel two-stage approach.
The paper tackles binary image skeletonization by using a 2-stage U-Net to split the problem into shape minimization and corrective skeleton thinning, resulting in visually better results than the baseline SkelNetOn model and proposing a new metric M-CCORR to address class imbalance issues.
Object Skeletonization is the process of extracting skeletal, line-like representations of shapes. It provides a very useful tool for geometric shape understanding and minimal shape representation. It also has a wide variety of applications, most notably in anatomical research and activity detection. Several mathematical algorithmic approaches have been developed to solve this problem, and some of them have been proven quite robust. However, a lesser amount of attention has been invested into deep learning solutions for it. In this paper, we use a 2-stage variant of the famous U-Net architecture to split the problem space into two sub-problems: shape minimization and corrective skeleton thinning. Our model produces results that are visually much better than the baseline SkelNetOn model. We propose a new metric, M-CCORR, based on normalized correlation coefficients as an alternative to F1 for this challenge as it solves the problem of class imbalance, managing to recognize skeleton similarity without suffering from F1's over-sensitivity to pixel-shifts.