We present a model for the automated segmentation of cells from confocal microscopy volumes of biological samples. The segmentation task for these images is exceptionally challenging due to weak boundaries and varying intensity during the imaging process. To tackle this, a two step pruning process based on the Fast Marching Method is first applied to obtain an over-segmented image. This is followed by a merging step based on an effective feature representation. The algorithm is applied on two different datasets: one from the ascidian Ciona and the other from the plant Arabidopsis. The presented 3D segmentation algorithm shows promising results on these datasets.