In this paper we address the problem of cells detection from mi-croscopy images. We construct a dictionary of candidate shapes obtained from previous segmentation maps and define an energy function to select the best candidates. The energy minimization is performed by an iterative graph cut algorithm. The proposed approach optimally combines the segmentation maps obtained with different methods and/or parameters. We show on synthetic and real data that this process allows to drastically improve the performance of each individual segmentation.