Optimal cut in minimum spanning trees for 3-D cell nuclei segmentation

In biology and pathology immunofluorescence microscopy approaches are leading techniques for deciphering of the molecular mechanisms of cell activation and disease progression. Although several commercial softwares for image analysis are presently in the market, available solutions do not allow a totally non subjective image analysis. There is therefore strong need for new methods that could allow a completely non-subjective image analysis procedure including for thresholding and for choice of the objects of interest. To address this need, we describe a fully automatic segmentation of cell nuclei in 3-D confocal immunofluorescence images. The method merges segments of the image to fit with a nuclei model learned by a trained random forest classifier. The merging procedure explores efficiently the fusion configurations space of an over-segmented image by using minimum spanning trees of its region adjacency graph.