Reliable AI-Assisted OCT Interpretation
Expert Analysis:
Managed by certified, rigorously trained image graders.
Secure Infrastructure:
Supported by a seamless, fully encrypted database link.
Client-Centric Process:
Focused on transparent communication and flexible study workflow.
Secure sharing De-identified data only
FOR RESEARCH & INVESTIGATIONAL USE ONLY
FREE, NO COMMITMENT
See it on your own data — free
Send one de-identified volume. We run it and send your sample report, so you know exactly what you'd be licensing.
1
Upload via a secure link
Send a link to one de-identified OCT volume via the form below.
2
We run the segmentation
Our team runs your scan through JDAe, our automated segmentation engine.
3
Get your report next day
Receive a sample report - thickness maps, ETDRS-grid values, and full macula data.
4
Book a call if you're ready to license for in-house use
Like what you see? License the tools and run them yourself, unlimited, in-house.
THE SOFTWARE
Two tools, one workflow
JDAe - Data analysis with AI-based segmentation
Get structured, trial-ready output in one pass
Read Cirrus, Spectralis, and Neuro-i OCT data
Segment 8+ retinal layers automatically
Measure average thickness for 9 standard ETDRS subfields and thickness map
Export deterministic results in PDF report
JAM - Manual Correction
Precise by Hand, Simple by Design
Correct an existing layer boundary
Annotate pathological regions
Export the corrected data in JSON file
INCLUDED WITH A LICENSE
See it on your own data - free
What you get
Send one de-identified volume. We run it and send your sample report, so you know exactly what you'd be licensing.
Cross-vendor analysis
One engine across Cirrus and Spectralis, so multi-site data is comparable.
Structured outputs
Per-layer thickness, ETDRS sectorization, PDF report + CSV
Human-in-the-loop
QC
Correct the algorithm's blind spots on retinas with significant pathology.
Audit trail
Grader identity, timestamps, and edit history on every corrected dataset.
Clinically Validated
Built on peer-reviewed research
JuneBrain's automated segmentation is built on a deep-learning method published in Medical Image Analysis (2021). It produces smooth, anatomically ordered retinal layer surfaces with sub-pixel accuracy, and was validated against expert manual delineation on healthy, multiple sclerosis, and diabetic macular edema OCT data - meeting or exceeding established methods.
He Y, Carass A, Liu Y, et al. Structured layer surface segmentation for retina OCT using fully convolutional regression networks. Med Image Anal. 2021;68:101856.
PRICING
Simple, transparent pricing
One plan for research and investigational use. Annual license, billed yearly.
FOR RESEARCH & INVESTIGATIONAL USE ONLY