Hierarchical Random Walker Segmentation for Large Volumetric Biomedical Images

TL;DR

This paper introduces a hierarchical random walker segmentation framework that significantly reduces runtime and memory usage, enabling interactive segmentation of large 3D biomedical images in real-time.

Contribution

It presents the first hierarchical approach to overcome the linear complexity of the random walker algorithm, achieving sublinear runtime and constant memory for large datasets.

Findings

Achieves sublinear run time and constant memory complexity.

Maintains high segmentation quality on synthetic and real datasets.

Enables interactive segmentation on datasets of hundreds of gigabytes.

Abstract

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/).