Random-walk Segmentation of Nuclei in Fluorescence Microscopic Images with Automatic Seed Detection

Tabea Margareta Grace Pakull, Frederike Wirth, Klaus Brinker

Abstract: In personalized immunotherapy against cancer analysis of cell nuclei in tissue samples can provide helpful information to predict whether the benefits of the therapy outweigh the usually severe side effects. Since segmentation of nuclei is the basis for all further analyses of cell images, research into suitable methods is of particular relevance. In this paper we present and evaluate two versions of a segmentation pipeline based on the established random-walk method. These versions contain automatic seed detection, using a distance transformation in one of them. In addition, we present a method to select the required hyper-parameter of the random-walk algorithm. The evaluation using a benchmark dataset shows that promising results can be achieved with respect to common evaluation metrics. Furthermore, the segmentation accuracy can compete with a reference CellProfiler segmentation pipeline, based on the watershed transformation. Based on the presented pipeline, the random-walk metho

External IDs:dblp:conf/biostec/PakullWB22