3D Cell Nuclei Segmentation with Balanced Graph Partitioning
This addresses the need for better and faster segmentation methods for large 3D biomedical images, though it appears incremental as it builds on existing graph-based approaches.
The authors tackled the problem of 3D cell nuclei segmentation in biomedical images by proposing a new algorithm using recursive balanced graph partitioning, which resulted in faster speed with similar or better quality and acceptable memory overhead compared to state-of-the-art methods.
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.