AutoContour
Overview
AutoContour by Radformation is a deep learning automated AI contouring software designed to enhance efficiency and safety in cancer treatment planning. It provides fast, consistent, and standardized contouring with planning-ready results in minutes, eliminating the need for extensive setup. The software comes equipped with a vast library of AI-trained models, including over 480 models across CT, MR, and CBCT modalities, with a focus on organs at risk (OARs) and lymph nodes.
AutoContour streamlines the radiation therapy workflow by automatically segmenting common structures and generating necessary planning volumes like expansions, booleans, crops, and ring structures. It supports both rigid and deformable registration, enabling accurate anatomical mapping and dose accumulation for re-irradiation and adaptive planning workflows. The software integrates seamlessly with existing treatment planning systems, such as Eclipse™, and other DICOM-compatible systems.
The platform includes a built-in automated review process with intuitive editing tools, ensuring clinical experts maintain control and can make immediate adjustments. AutoContour’s models are developed in alignment with international consensus guidelines, including AAPM TG-263 nomenclature, RTOG, and NRG, promoting high-quality and consistent contours.
Radformation’s cloud-based AI segmentation engine processes de-identified CT data, ensuring patient privacy, and returns contours to the local client application for user review and export as DICOM RTSTRUCT files. This architecture eliminates the need for expensive local hardware and extensive IT support.
Reviewed by Pouyan Golshani, MD — Interventional Radiologist
Key Features
- AI-driven deep learning contouring for OARs and lymph nodes
- 480+ AI-trained models across CT, MR, and CBCT
- Generates planning-ready contours in minutes
- Seamless integration with Eclipseu2122 and DICOM-compatible systems
- Automated planning structure generation with site-specific templates
- Rigid and deformable image registration capabilities
- Multi-modality support (CT, MR, CBCT, PET/CT for registration/fusion)
- Built-in automated review process with comprehensive editing tools
- Dose accumulation and evaluation for re-irradiation and adaptive planning
- Adherence to international consensus guidelines (AAPM TG-263, RTOG, NRG)
Use Cases
- Automating organ-at-risk (OAR) and target volume segmentation in radiation therapy
- Streamlining and standardizing radiation treatment planning workflows
- Facilitating adaptive planning and re-irradiation through deformable registration and dose accumulation
- Reducing manual contouring time and improving consistency across planners
- Generating complex planning structures (expansions, booleans, crops, rings) automatically
- Supporting clinical evaluation and quality improvement projects in radiation oncology
What Physicians Need to Know
Leverage AutoContour to significantly reduce the tedious manual contouring time, allowing for more focus on complex cases and patient interaction. While the AI generates high-quality contours, always perform a thorough review, especially for critical structures or unusual anatomies, to ensure clinical accuracy and patient safety. Utilize the automated planning structure generation and template features to standardize workflows and maintain consistency across your department. Explore the deformable registration capabilities for adaptive planning, particularly in cases with significant anatomical changes like head and neck. Integrate it seamlessly into your existing TPS to maximize efficiency.
AutoContour is designed for flexible integration. It offers deep integration with Varian Eclipse (v15.5+) using ESAPI, enabling a streamlined workflow directly within the TPS. For other treatment planning and imaging systems, it functions as a standalone application, communicating via DICOM. This vendor-neutral approach ensures compatibility across diverse clinical environments, allowing clinics to enhance their workflow without being tied to a single vendor's ecosystem. It also integrates with other Radformation products like ClearCheck for a comprehensive automation suite.
Details
| Category | Oncology AI, Radiology & Imaging AI |
| Pricing | Contact for pricing — Annual or multi-year contracts; pricing reflects comprehensive product nature |
| Deployment | Hybrid (cloud-based AI engine with local client application) |
| Compliance | |
| BAA Available | Unknown AI-estimated |
| HIPAA Compliant | Unknown AI-estimated |
| FDA Status |
1 AI-estimated AutoContour (Model RADAC V4) received FDA 510(k) clearance (K242729) on December 9, 2024. It is radiological image processing software for radiation therapy, intended to assist treatment planners in contouring and reviewing structures within medical images for treatment planning. It uses DICOM-compliant CT or MR image data as input to automatically contour structures using machine learning, allows user modification, and generates DICOM-compliant structure set data. |
| Integrations | |
| EHR | Not specified |
| Specialties | Nuclear Medicine, Oncology, Radiology |
What the Web Says
AutoContour by Radformation is an AI-driven automated contouring software designed for radiation oncology, aiming to streamline the treatment planning process. It utilizes deep learning to generate contours for organs at risk (OARs) and planning structures across various imaging modalities like CT, MR, and CBCT. Physicians and medical physicists generally report that AutoContour significantly reduces contouring time and improves consistency compared to manual methods, though some level of review and occasional editing by a clinical expert remains crucial.
Overall: PositiveStrengths
- Significant time savings in contouring, potentially reducing total contouring time by up to 84%.
- Generates consistent and standardized contours, reducing inter-observer variability.
- Offers a large library of AI-trained models (480 models, including 115 lymph node models) across multiple modalities (CT, MR, CBCT).
- Includes automated generation of planning structures (expansions, booleans, crops, ring structures) using site-specific templates.
- Seamless integration with Eclipse and DICOM-compatible systems, with additional efficiency when integrated with ClearCheck.
- Features built-in tools for efficient review and editing of AI-generated contours, including single-click structure review and automatic viewing plane alignment.
Limitations
- AI-generated contours sometimes require edits, and a smooth process for review and editing is essential.
- Some users have noted that the software requires the user's attention and fills an entire computer screen during calculation, requiring review of every slice, which can be time-consuming.
- In a minority of cases, unusual anatomy can lead to poor scores, requiring manual contouring and more in-depth physician review.
- One Reddit user expressed a preference for Limbus AI contouring over Radformation's, citing better accuracy and a more efficient workflow.
- While generally positive, a study indicated that for certain organs like the prostate, physicians should continue to manually review and edit contours based on MRI.
- The clinical applicability of geometric outcome metrics, often reported in studies, has been questioned by researchers.
Based on reviews from: Radformation.com, Reddit (r/MedicalPhysics), PubMed Central (PMC), EMJ (European Medical Journal), The Royal College of Radiologists, ResearchGate, RT Medical Systems
Last updated: 2026-07-20
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Press & Coverage
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Product demos, reviews, and walkthroughs for AutoContour.
AutoContour & ClearCheck
Radformation
Radformationu3163Introducing AutoContour: Automated AI Contouring Software
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Dosimetry Webinar 2025 - Mastering AutoContour
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CARO 2025 - AutoContour & ClearCheck
Radformation
Introducing AutoContour: Automated AI Contouring Software
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Head & Neck Organs-at-Risk: Consensus vs. Radformation AutoContour
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