Abstract
Three dimensional 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 clump 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 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. Although proposed strategies have been developed and tested on data collected through fluorescence microscopy, they are applicable to other problems in low level vision and medical imaging.
The Research was supported by National Aeronautics and Space Administration Grant no. T6275W, NASA Specialized Center for Research in Radiation Health Effects, the low dose radiation research program and medical imaging program at the Office of Biological Effects Research U.S. Department of Energy, Grant No. DE-FG03-01ER63240. PubID is LBNL-61402.
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© 2006 Springer-Verlag Berlin Heidelberg
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Han, J., Chang, H., Yang, Q., Barcellos-Hoff, M.H., Parvin, B. (2006). 3D Segmentation of Mammospheres for Localization Studies. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2006. Lecture Notes in Computer Science, vol 4291. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11919476_52
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DOI: https://doi.org/10.1007/11919476_52
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