SEGMENTATION OF MAMMOSPHERE STRUCTURES FROM VOLUMETRIC DATA

3D cell culture assays have emerged as the basis of an improved model system for evaluating therapeutic agents, molecular probes, and exogenous stimuli. However, there is a gap in robust computational techniques for segmentation of image data that are collected through confocal or deconvolution microscopy. The main issue is the volume of data, overlapping subcellular compartments, and variation in scale and size of subcompartments of interest. A geometric technique has been developed to bound the solution of the problem by first localizing centers of mass for each cell and then partitioning clumps of cells along minimal intersecting surfaces. An approximate solution to the center of mass is realized through iterative spatial voting, which is tolerant to variation in shape morphologies and overlapping compartments and is shown to have an excellent noise immunity. These approximate estimates to centers of mass are then used to partition a clump of cells along minimal intersecting surfaces that are estimated by Radon transform. Examples on real data and performance of the system over a large population of data are evaluated. Furthermore, it is shown that the proposed methodology is extensible in terms of its application to protein localization studies