Coupled Segmentation of Nuclear and Membrane-bound Macromolecules through Voting and Multiphase Level Set

Area of Science:

• Cell Biology
• Biophysics
• Image Analysis

Background:

• Membrane-bound macromolecules are vital for tissue architecture and cell-cell communication, regulated by a significant portion of the genome.
• Accurate segmentation of cell surface proteins is essential for quantitative analysis but is hindered by discontinuous signals, heterogeneous intensity, and nuclear clumping.

Purpose of the Study:

• To develop and validate a robust computational method for segmenting membrane proteins on a cell-by-cell basis.
• To address the challenges of discontinuous membrane signals and complex nuclear structures in image analysis.
• To enable quantitative assessment of membrane protein localization for comparative biological studies.

Main Methods:

• A three-step computational approach involving iterative tangential voting for signal regularization.
• Utilizing nuclear features and Delaunay triangulation for segmenting clumped nuclei to constrain surface protein localization.
• Employing multi-phase geodesic level-set segmentation for assigning membrane-bound macromolecules to individual cells.

Main Results:

• The proposed method successfully regularizes discontinuous membrane signals.
• It effectively constrains surface protein localization by incorporating nuclear features, even in cases of clumped nuclei.
• The multi-phase geodesic level-set approach accurately assigns membrane proteins to individual cells.

Conclusions:

• The developed three-step method provides a robust solution for segmenting cell surface proteins.
• It demonstrates superior performance in quantitative analysis of membrane protein localization compared to existing methods.
• This technique facilitates a deeper understanding of cell-cell communication and tissue organization through precise macromolecule assessment.