PT - JOURNAL ARTICLE AU - A. Browet AU - C. De Vleeschouwer AU - L. Jacques AU - N. Mathiah AU - B. Saykali AU - I. Migeotte TI - Cell Segmentation with Random Ferns and Graph-cuts AID - 10.1101/039958 DP - 2016 Jan 01 TA - bioRxiv PG - 039958 4099 - http://biorxiv.org/content/early/2016/02/17/039958.short 4100 - http://biorxiv.org/content/early/2016/02/17/039958.full AB - The progress in imaging techniques have allowed the study of various aspect of cellular mechanisms. To isolate individual cells in live imaging data, we introduce an elegant image segmentation framework that effectively extracts cell boundaries, even in the presence of poor edge details. Our approach works in two stages. First, we estimate pixel interior/border/exterior class probabilities using random ferns. Then, we use an energy minimization framework to compute boundaries whose localization is compliant with the pixel class probabilities. We validate our approach on a manually annotated dataset.