An Enhanced Multi-label Random Walk for Biomedical Image Segmentation Using Statistical Seed Generation

Image segmentation is one of the fundamental problems in biomedical applications and is often mandatory for quantitative analysis in life sciences. In recent years, the amount of biomedical image data has significantly increased, rendering manual segmentation approaches impractical for large-scale studies. In many cases, the use of semi-automated techniques is convenient, as those approaches allow to incorporate domain knowledge of experts into the segmentation process. The random walker framework is among the most popular semi-automated segmentation algorithms, as it can easily be applied to multi-label situations. However, this method usually requires manual input on each individual image and, even worse, for each disconnected object. This is problematic for segmenting multiple unconnected objects like individual cells, or very fine anatomical structures. Here, we propose a seed generation scheme as an extension to the random walker framework. Our method needs only few manual labels to generate a sufficient number of seeds for reliably segmenting multiple objects of interest, or even a series of images or videos from an experiment. We show that our method is robust against parameter settings and evaluate the performance on both synthetic as well as real-world biomedical image data.