Developments in optical microscopy imaging have generated large high-resolution datasets that have spurred medical researchers to conduct investigations into mechanisms of disease, including cancer, at cellular and sub-cellular levels. The work reported here demonstrates that a suitable methodology can be conceived which isolates modality- dependent effects from the larger segmentation task and that 3D reconstructions can be cognizant of shapes as evident in the available 2D planar images. In the current realization, a method based on active geodesic contours is first deployed to counter the ambiguity that exists in separating overlapping cells on the image plane. Later, another segmentation effort based on a variant of Voronoi tessellations improves the delineation of the cell boundaries using a Bayesian formulation. In the next stage, the cells are interpolated across the third dimension thereby mitigating the poor structural correlation that exists in that dimension. We deploy our m...