3D Cell Nuclei Segmentation with Balanced Graph Partitioning

TL;DR

This paper introduces a novel 3D cell nuclei segmentation algorithm that employs recursive balanced graph partitioning, achieving faster processing with comparable or improved accuracy on simulated biomedical images.

Contribution

The paper presents a new graph partitioning-based segmentation method that overcomes computational costs of existing approaches, specifically tailored for 3D biomedical images.

Findings

Faster segmentation compared to state-of-the-art methods

Maintains similar or better segmentation quality

Has acceptable memory overhead

Abstract

Cell nuclei segmentation is one of the most important tasks in the analysis of biomedical images. With ever-growing sizes and amounts of three-dimensional images to be processed, there is a need for better and faster segmentation methods. Graph-based image segmentation has seen a rise in popularity in recent years, but is seen as very costly with regard to computational demand. We propose a new segmentation algorithm which overcomes these limitations. Our method uses recursive balanced graph partitioning to segment foreground components of a fast and efficient binarization. We construct a model for the cell nuclei to guide the partitioning process. Our algorithm is compared to other state-of-the-art segmentation algorithms in an experimental evaluation on two sets of realistically simulated inputs. Our method is faster, has similar or better quality and an acceptable memory overhead.