Reconstruction of cellular biological structures from optical microscopy data

Kishore Mosaliganti*, Lee Alex Donald Cooper, Richard Sharp, Raghu Machiraju, Gustavo Leone, Kun Huang, Joel Saltz

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

25 Scopus citations

Abstract

Developments in optical microscopy imaging have generated large high-resolution data sets that have spurred medical researchers to conduct investigations into mechanisms of disease, including cancer at cellular and subcellular levels. The work reported here demonstrates that a suitable methodology can be conceived that 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 methods on three separate data sets obtained from light, confocal, and phase-contrast microscopy and validate the results appropriately.

Original languageEnglish (US)
Article number4445666
Pages (from-to)863-876
Number of pages14
JournalIEEE Transactions on Visualization and Computer Graphics
Volume14
Issue number4
DOIs
StatePublished - Jul 1 2008

Keywords

  • Cellular reconstruction
  • Microscopic imaging
  • Segmentation
  • Tessellations

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Computer Graphics and Computer-Aided Design

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