CYTOISED Digital Pathology Suite

See What the
Stain Hides.

CYTOISED applies physics-based spectral analysis to histopathology—delivering automated nuclear segmentation, biomarker quantification, and lesion grading without stain-dependent training data.

The Problem: Every Lab Stains Differently

Staining protocols vary between labs, scanners, and technicians—creating 25–40% inter-observer variability in complex grading. Deep learning tools trained on one lab's stains fail on another's, requiring $500K+ per tumor type to retrain.

"CYTOISED reads the physics of the tissue, not the color of the dye."

5
Stain Protocols Built-In
Zero
Retraining Required

Why AI Pathology Tools Fail

Deep learning models trained on one staining protocol collapse when transferred to a different lab's preparation.

Automated Cell Detection

9 segmentation methods—from classical watershed to physics-based ISED to deep learning ensembles—with automatic method selection based on tissue type and stain quality.

→ Nuclei, Cells, Tissue, RBCs, Glands

Clinical Scoring

Built-in biomarker quantification: Ki-67 proliferation index, H-score, Allred score, Gleason grading, Nottingham grading—exportable to CSV, Excel, or PDF.

How It Works

Three physics-grounded steps. No retraining. No black boxes.

1

Decompose

Each pixel is separated into spectral opponent channels—isolating nuclear stain (hematoxylin) from cytoplasmic stain (eosin) using physics, not learned patterns.

2

Segment

Nine detection methods—from watershed to ISED to deep learning ensembles—are automatically selected based on tissue type and stain quality.

3

Score

Clinical biomarkers—Ki-67, H-score, Allred, Gleason—are computed and exported automatically. Every result is fully interpretable.

Scientific Validation

Segmentation Quality
SEGMENTATION QUALITY: VERIFIED

+17% Stage 1 Cancer Detection

ISED-based spectral analysis detects early-stage malignancies that conventional thresholding methods miss, with documented improvement in Stage 1 detection rates.

+44% Edge Preservation

Nuclear boundary detection achieves 44% better edge preservation over standard Otsu thresholding, critical for accurate morphometric analysis and grading.

How We Compare

What We Measure AI-Only Tools CYTOISED Why It Matters
Retraining Per Stain $500K+ per tumor type None Required 5 stain protocols supported out of the box
Early Cancer Detection Baseline +17% Stage 1 Documented improvement in detection rates
Nuclear Edge Preservation Otsu thresholding +44% Better Critical for accurate morphometric grading
Explainability Black box Fully Interpretable Every operation has physical meaning—regulatory advantage

Stain Normalization

Supports H&E (Ruifrok, Macenko, Vahadane), Pap, and Trichrome—standardizing across labs and scanners.

Biomarker Quantification

Ki-67, H-score, Allred, mitotic count, N:C ratio—12+ clinical metrics, export-ready.

Tumor Microenvironment

Spatial analysis with Voronoi density, clustering index, and tumor-stroma ratio mapping.

CYTOISED in Action

H&E ANALYSIS DEMO

Recording

Bring CYTOISED to Your Lab

CYTOISED is built on validated mathematics and ready for research collaboration. Whether you're a pathology department, a diagnostics company, or a research institution — we're looking for partners to help bring this technology to clinical practice.