From grayscale scan to anatomical map
The system converts dense OCT textures into interpretable retinal regions, helping the audience understand where layers begin, end, and change across a macular scan.
AI-powered OCT analysis
See every retinal layer.
Understand every scan.
A presentation-ready retinal OCT intelligence experience for segmenting anatomical layers and fluid-related regions with fast, consistent visual interpretation.
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110annotated OCT B-scans
10DME patient studies
0.957reported final Dice coefficient
Segmentation modeLayer map
Scan modeMacular B-scan
Layer delineation
Pixel-wise segmentation presents the retina as distinct anatomical bands rather than an undifferentiated image.
Fluid-aware analysis
Colored regions make pathological spaces visually apparent and easier to compare with expert annotation.
Rapid review
Consistent output supports faster scan review, training demonstrations, and structured research workflows.
Interactive dataset experience
Animated macular OCT sweep
Move through eleven B-scans surrounding the foveal center. The viewer preserves the entire source image, adds a scanner beam, and lets you pause or inspect individual frames.
ILM
RPE
Five-stage intelligence pipeline
How the segmentation engine works
A carefully structured encoder-decoder workflow transforms a noisy B-scan into pixel-wise retinal labels while retaining fine anatomical boundaries.
Acquire
Load an OCT B-scan and standardize its spatial format.
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Denoise
Reduce speckle while protecting clinically important edges.
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Encode
Extract multi-scale texture, boundary, and context features.
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Decode
Restore spatial detail and predict dense class probabilities.
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Visualize
Display retinal layers and fluid regions as a color map.
Input crop
Before denoising
Noise-aware
preprocessing
preprocessing
Processed crop
After denoising
Anatomy made visible
Eight-class retinal layer map
Each color represents a different anatomical or pathological region. Hover or tap a label to spotlight its corresponding band in the stylized retinal map.
Foveal center
Visual evidence
Prediction, annotation, and model behavior
Repository visuals are displayed at their full aspect ratio with contain-fit framing so no anatomical detail or label is cropped.
Comparative segmentation panel
One scan. Multiple methods. Clear visual differences.
The panel brings the test B-scan, two expert annotations, the AI prediction, and several comparative methods into a single presentation-ready view.
2expert annotations
8visual classes
1unified review panel
Prediction review
Prediction vs ground truth
The side-by-side masks highlight boundary agreement and visible class differences.
Optimization history
Training curves
Dice stabilizesLoss declinesFinal reported Dice: 0.957
Presentation value
Built to make retinal AI immediately understandable
A polished visual story for clinical demos, instrument presentations, research discussions, and AI capability showcases.
Clinician-friendly output
Color-coded anatomy is easier to discuss than raw prediction arrays or abstract model architecture.
Instrument-ready narrative
The scan-to-insight workflow naturally complements OCT systems and ophthalmic imaging portfolios.
Research transparency
Expert labels, predictions, loss curves, and side-by-side comparisons remain visible in one coherent presentation.
Offline demonstration
All images, controls, animations, and brand assets are stored locally for dependable client presentations.
Ophthalmic intelligence, clearly presented
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Turn every OCT scan into a visual story.
Explore retinal layers, fluid regions, expert comparisons, and model behavior in one presentation experience.