Medical imaging and radiology
Aidoc
Clinical AI platform for radiology and enterprise imaging workflows.
Last updated: June 5, 2026
Back to directoryRadformation AutoContour medical AI product profile
AI auto-contouring software for radiation therapy workflows that generates guideline-oriented structures for clinician review before treatment planning.
Medical imaging and radiology
Best fit
Radiation oncology programs that need faster organ-at-risk contouring and can commission, review, edit, and monitor AI-generated structures before planning.
- Primary use case
- Deep-learning auto-contouring, organ-at-risk segmentation, treatment-planning structure generation, DICOM workflow integration, and radiation oncology planning acceleration
- Audience
- Radiation oncology departments, dosimetry teams, medical physicists, treatment-planning leaders, and oncology AI governance committees
- Risk level
- High
- Pricing signal
- Enterprise radiation oncology pricing; verify current AutoContour version, Limbus integration, deployment model, support, and treatment-planning system integration terms.
- Official sources
- 4 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 Radformation AutoContour as safe for a local clinical, operational, or research workflow.
| Regulatory / FDA | Treat as high-risk radiological image processing software for radiation therapy and verify the exact 510(k), version, modality, anatomy, region, and intended-use labeling before production use. |
|---|---|
| Privacy | Review image routing, DICOM metadata, support access, cloud or local processing, authentication, retention, audit logs, BAA terms, and whether patient data contributes to model updates. |
| Evidence | Commission site-by-site performance against local CT, MR, CBCT, scanner protocols, artifacts, abnormal anatomy, physician edits, plan dosimetry, and interobserver variation. |
| Workflow | Best governed as a planning accelerator with documented human review, contour editing, physicist/dosimetrist QA, fallback manual contouring, and monitoring for drift in edit burden. |
Where Radformation AutoContour fits
Radformation describes AutoContour as AI-driven deep-learning contouring software for radiation oncology, with hundreds of CT, MR, and CBCT structure models, DICOM-compatible workflows, Eclipse integration, and guideline-aligned naming; FDA records and Radformation materials should be checked for the exact cleared version and region.
Not for: Autonomous target delineation, treatment planning without physician and dosimetrist review, or use outside the current cleared version, modality, anatomy, and labeling.
What to verify before using Radformation AutoContour
- Current FDA 510(k), local regulatory status, AutoContour version, supported CT/MR/CBCT inputs, structure library, and whether Limbus-derived features are included.
- Treatment-planning system integration, DICOM routing, Eclipse or DICOM-compatible workflow, zero-click setup, user permissions, audit trails, and downtime fallback.
- How patient images, contours, support access, logs, cloud or local processing, retention, and model-improvement data use are handled.
- Local commissioning results for each disease site, contour edit burden, outlier anatomy, image artifacts, lymph-node models, physician acceptance, and downstream plan-quality effects.
Source links
Use these links to confirm current claims, terms, regulatory status, pricing, and deployment requirements.
- radformation.com / autocontour
- resources.radformation.com / autocontour-white-paper
- radformation.com / press / radformation-regulatory-clearances
- www.oit.va.gov / services / trm
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