Binary Image Skeletonization Using 2-Stage U-Net

TL;DR

This paper introduces a novel 2-stage U-Net approach for binary image skeletonization, significantly improving visual results and proposing a new correlation-based metric to address class imbalance issues.

Contribution

It presents a new deep learning architecture for skeletonization and a novel evaluation metric, enhancing accuracy and robustness over existing methods.

Findings

Results are visually superior to baseline models.

The proposed M-CCORR metric effectively handles class imbalance.

The 2-stage U-Net improves shape minimization and skeleton thinning.

Abstract

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.