Hierarchical Random Walker Segmentation for Large Volumetric Biomedical Images
This enables interactive segmentation for large out-of-core biomedical datasets, addressing a bottleneck in current research, though it is incremental as it focuses on efficiency rather than improving segmentation quality.
The paper tackles the impractical run time and memory requirements of the random walker method for segmenting large 3D biomedical images by proposing a hierarchical framework, achieving sublinear run time and constant memory complexity, with incremental updates in seconds for volumes up to hundreds of gigabytes.
The random walker method for image segmentation is a popular tool for semi-automatic image segmentation, especially in the biomedical field. However, its linear asymptotic run time and memory requirements make application to 3D datasets of increasing sizes impractical. We propose a hierarchical framework that, to the best of our knowledge, is the first attempt to overcome these restrictions for the random walker algorithm and achieves sublinear run time and constant memory complexity. The goal of this framework is -- rather than improving the segmentation quality compared to the baseline method -- to make interactive segmentation on out-of-core datasets possible. The method is evaluated quantitavely on synthetic data and the CT-ORG dataset where the expected improvements in algorithm run time while maintaining high segmentation quality are confirmed. The incremental (i.e., interaction update) run time is demonstrated to be in seconds on a standard PC even for volumes of hundreds of gigabytes in size. In a small case study the applicability to large real world from current biomedical research is demonstrated. An implementation of the presented method is publicly available in version 5.2 of the widely used volume rendering and processing software Voreen (https://www.uni-muenster.de/Voreen/).