QuPath Native Version vs TerryDR PhenoCluster + Spa Plugins: Performance Comparison

Core Breakthroughs of PhenoCluster

1. Efficiency Revolution (20-100x Acceleration)

• Algorithm Optimization: Replacing traditional threshold segmentation with anti-interference deep learning models, reducing the analysis time of a single sample from hours to minutes.

• Artificial Intelligence Model + Hardware Collaboration: Automated recognition + GPU acceleration increases the speed of cell clustering calculation for 60+ markers by 50 times.

• Automated Process: Eliminating repetitive operations such as manual marking of positive/negative and adjusting thresholds, simplifying operations that originally required experts to three clicks.

2. Realization of Technology Democratization

• Zero-Code Operation: Completing cell subset definition through a drag-and-drop interface, no longer exclusive to bioinformatics experts.

• Intelligent Noise Resistance: Built-in signal spillover correction module, generally no manual intervention is needed for background subtraction.

Seamless Connection between PhenoCluster and Spa

1. Data Flow Integration

• Automatic Transfer of Analysis Results: The cell clustering data of PhenoCluster directly serves as the input of Spa, automatically triggering the microenvironment interaction analysis of Spa, avoiding format conversion in traditional processes (saving hours per sample).

• Spatial Marker Inheritance: Cell-level protein expression profiles and spatial coordinate information are retained simultaneously, supporting multi-scale analysis from subcellular (<1μm) to tissue-level (>1mm).

Examples of Image Analysis Technology Democratization

• The multiplex fluorescence data analysis that originally required a bioinformatics team to complete in 2 weeks can now be independently completed by 1 technician in 2 days.

• The steps of 40-marker phenotype analysis + spatial analysis have been reduced from more than 40 steps to 4 steps (select area → formulate rules → automatic analysis → export report or spatial analysis), and the time has been reduced from several days to several minutes.

• Expansion of User Groups: The independent usage rate of technical personnel in the third-party pathological service has increased from <5% to 62%, and the analysis error rate has decreased by 73%.