Medical imaging and radiology
Aidoc
Clinical AI platform for radiology and enterprise imaging workflows.
Last updated: June 5, 2026
Back to directoryRayStation Deep Learning Segmentation medical AI product profile
RaySearch RayStation module that uses deep-learning segmentation models to generate radiation therapy contours inside the treatment-planning environment.
Medical imaging and radiology
Best fit
Cancer centers already using or evaluating RayStation that want AI segmentation tightly integrated with treatment planning and can commission site-specific review workflows.
- Primary use case
- Deep-learning organ segmentation inside RayStation, radiation therapy contour generation, automated planning support, and treatment-planning workflow consistency
- Audience
- Radiation oncology departments, dosimetrists, medical physicists, treatment-planning administrators, and cancer-center AI governance committees
- Risk level
- High
- Pricing signal
- RayStation enterprise licensing varies by module, market, release, service contract, and treatment-planning configuration; verify current DLS availability and regulatory clearance.
- Official sources
- 5 official sources
Compare within workflow: Medical imaging and radiology · comparison shortlist · source index
Regulatory, privacy, evidence, and workflow lens
Product-specific review. These product-specific signals summarize what the cited sources imply before treating RayStation Deep Learning Segmentation as safe for a local clinical, operational, or research workflow.
| Regulatory / FDA | Treat as high-risk radiation oncology treatment-planning software and verify the exact RayStation release, 510(k) or local authorization, enabled model, modality, anatomy, and market restriction before production use. |
|---|---|
| Privacy | Review RayStation deployment architecture, image and contour storage, support access, scripting, logs, connected services, retention, BAA or data-processing terms, and whether model updates use customer data. |
| Evidence | Commission DLS performance by cancer site, scanner, protocol, contouring guideline, physician edit burden, abnormal anatomy, artifacts, and treatment-plan impact rather than relying only on vendor time-savings claims. |
| Workflow | Best governed as an integrated contouring and planning accelerator with radiation oncologist review, dosimetrist editing, physicist QA, documented exceptions, and manual fallback for unsupported cases. |
Where RayStation Deep Learning Segmentation fits
RaySearch describes machine-learning features in RayStation for automated plan generation and organ segmentation, with deep-learning segmentation models released with RayStation versions and subject to market-specific regulatory clearance; RaySearch's FDA clearance announcement and FDA 510(k) summary for RayStation 8.1 document machine-learning planning and segmentation functionality.
Not for: Unreviewed contour acceptance, autonomous treatment decisions, assuming dose-prediction claims apply in the U.S. or Canada, or use outside the cleared release, market, modality, and model catalogue.
What to verify before using RayStation Deep Learning Segmentation
- Current RayStation release, licensed machine-learning modules, FDA or local regulatory status, DLS model catalogue date, supported CT/MR scope, and market restrictions.
- How generated contours move through RayStation planning, clinician editing, approval, scripting, export, audit logs, and fallback manual contouring.
- Patient image handling, local versus connected services, support access, model catalogue updates, retention, cybersecurity controls, and contractual PHI protections.
- Local commissioning for each disease site, scanner protocol, contour guideline, outlier anatomy, edit burden, physician acceptance, and downstream plan-quality effects.
Source links
Use these links to confirm current claims, terms, regulatory status, pricing, and deployment requirements.
- www.raysearchlabs.com / machine-learning-in-raystation
- www.raysearchlabs.com / raystation
- www.raysearchlabs.com / media / press-releases
- www.accessdata.fda.gov / cdrh_docs / pdf19
- www.raysearchlabs.com / globalassets / digizuite
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