An approach based on two-dimensional graph theory for structural cluster detection and its histopathological...

Area of Science:

• Computational biology
• Image analysis
• Graph theory applications

Background:

• Accurate cell cluster detection is crucial for understanding tissue morphology and disease progression.
• Existing methods may struggle with complex spatial arrangements and overlapping cell populations.

Purpose of the Study:

• To develop a novel graph theory-based approach for detecting cell clusters in 2D tissue images.
• To analyze spatial properties and growth patterns of cell populations, exemplified by human fibrosarcoma.

Main Methods:

• Defining a graph where nodes represent cell nuclei and edges have attributes based on measurable cell features.
• Constructing minimum spanning trees (MSTs) using various weight functions on edge attributes.
• Analyzing MSTs and attributed MSTs via decomposition to identify areas with similar local properties.

Main Results:

• The method successfully detects clusters of cells with similar local properties within tissue images.
• Demonstrated ability to separate clusters of tumor cells growing in different directions.
• Enabled approximation of distinct tumor growth angles in fibrosarcoma samples.

Conclusions:

• The graph theory approach provides a robust framework for cell cluster identification and analysis in biological images.
• This method facilitates the characterization of complex spatial structures and directional growth patterns.
• The decomposition technique allows for the creation of hierarchical cluster structures (cluster trees).