NON PARAMETRIC CELL NUCLEI SEGMENTATION BASED ON A TRACKING OVER DEPTH FROM 3D FLUORESCENCE CONFOCAL IMAGES

Abstract : 3D cell nuclei segmentation from fluorescence microscopy images is a key application in many biological studies. We propose a new, fully automated and non parametric method that takes advantage of the resolution anisotropy in fluorescence microscopy. The cell nuclei are first detected in 2D at each image plane and then tracked over depth through a graph based decision to recover their 3D profiles. As the tracking fails to separate very close cell nuclei along depth, we also propose a corrective step based on an intensity projection criterion. Experimental results on real data demonstrate the efficacy of the proposed method.