EnglishViews: 0 Author: Site Editor Publish Time: 2025-07-16 Origin: Site
Cell phenotype analysis: Automatically parse 60+ protein markers in multiplex immunofluorescence / flow mass spectrometry images, and accurately identify lymphocytes and their subsets, tumor cells and other types.
Spatial interaction modeling: Quantify cell neighborhood relationships (such as immune cell - tumor distance), immune exclusion zones and infiltration hotspots.
Human expert - level output: Reach the level of traditional manual analysis. One click directly generates pathological spatial biology analysis reports, while Halo / QuPath takes 3 hours.
It is equivalent to upgrading pathological analysis from "manual carving" to "industrial - grade 3D printing".
200 million cell annotation database: Trained based on mIF (multiplex immunofluorescence) and CyTOF (imaging mass spectrometry) images, accumulating 10,000 times more experience than human experts.
Skip the trap of manual parameter adjustment: Traditional methods need to repeatedly fine - tune threshold classifiers or Object classifiers. PhenoCluster directly outputs results based on AI, avoiding human errors.
Zero - configuration startup: Out - of - the - box models save more than 95% of operation steps.
It is like replacing manual driving with Tesla autopilot technology.
Annotate the analysis region (ROI) and segment cells in QuPath (QuPath native function)
Input the predefined rule table (for example: CD3 + CD4 + = Th cells)
Automatically generate cell phenotype analysis results and spatial interaction maps (such as cell neighborhood networks, immune exclusion heat maps)
Master it proficiently in 15 minutes without algorithm background.
StainSync:
Complete multi - modal slice registration (H&E / IHC / mIF / Visium) in 5 minutes
Solve the core challenge faced by most fluorescence scanners on the market: unable to achieve spatial alignment of multi - round cyclic staining images, such as image displacement deviation and deformation distortion.
CellSage:
Automatically classify H&E slices into 5 categories: tumor cells, immune cells, dead cells, etc.
Transformer architecture pre - trained model, zero annotation startup.
PhenoCluster + Spa:
Undertake the previous results and output spatial biology reports.
Form a fully automatic pipeline of "registration → classification → analysis".
Dual core capabilities:
Cell segmentation: Accurately locate each cell in the tissue.
Intelligent classification: Identify tumor cells, inflammatory cells, connective tissues, etc.
Seamless connection with spatial analysis:
Output cell coordinates + types, directly connect to the Spa plug-in to generate spatial interaction networks (such as TILs dynamic distribution maps).
Support for clinical diagnosis:
Support manual correction and multi - person annotation to ensure the consistency of results.
10 times efficiency improvement: AI replaces manual threshold fine - tuning.
Support for precision medicine: Spatial heterogeneity index guides treatment decisions.
Platform unbounded: Compatible with more than 20 devices to build unified analysis standards.
