Auto Segmentation

by GE Medical Systems  · Based in United States →
Oncology Radiology
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Regulatory Status Disclosed

Overview

Auto Segmentation, developed by GE Medical Systems (now part of GE HealthCare), is a post-processing software application designed to assist clinicians in radiation therapy planning. It leverages deep learning algorithms to automatically generate contours of organs at risk (OARs) from Computed Tomography (CT) images. The output is provided in the form of a DICOM Radiotherapy Structure Set (RTSS) series. This tool is intended to streamline the process of OAR delineation, accelerate workflow, and standardize outputs, ultimately aiding dosimetrists, medical physicists, and radiation oncologists. It is designed for use on the Edison HealthLink platform, with the user retaining responsibility to verify and correct the processed output contours as needed for accurate radiation therapy planning.

Reviewed by Pouyan Golshani, MD — Interventional Radiologist

Key Features

  • Automated contour generation of organs at risk (OARs)
  • Deep learning algorithm-based segmentation
  • Generates DICOM Radiotherapy Structure Set (RTSS) series
  • Assists in radiation therapy planning
  • Standardizes OAR delineation
  • Accelerates workflow for radiation therapy planning
  • Post-processing application for CT images
  • Designed for use on Edison HealthLink (EHL)
  • User verification and editing of contours

Use Cases

  • Radiation therapy planning
  • Organ at risk (OAR) delineation
  • Streamlining radiotherapy workflows
  • Enhancing consistency in treatment planning
  • Reducing manual contouring time
  • Initial anatomy segmentation on CT images

What Physicians Need to Know

Key Capabilities
Automates and standardizes organ segmentation, particularly for Organs at Risk (OARs) in radiation therapy planning using CT and MR images (e.g., MR Contour DL). Also provides auto lesion segmentation and measurements for ultrasound (e.g., breast, liver, thyroid, left ventricle) and supports full-body X-ray segmentation.
Clinical Utility
Streamlines radiation therapy planning by reducing manual contouring time and improving consistency. Enhances workflow efficiency, reduces physician effort in tumor burden analysis, and aids in confident decision-making. Helps minimize physical and cognitive fatigue for sonographers and can significantly shorten time from simulation to treatment planning.
Integration Options
Designed for seamless integration into existing radiotherapy workflows and is interoperable with third-party devices and software (e.g., MVision Contour+, MIM Contour Protu00e9gu00e9AI+u2122). The iRT solution connects with EMRs and OIS, and App Orchestrator facilitates access and management of multiple AI solutions from various vendors. GE HealthCare also collaborates with partners like Spectronic Medical AB, Amazon Web Services, and NVIDIA for AI development and integration.
Compliance Status
GE HealthCare's Auto Segmentation has received U.S. FDA 510(k) clearance, having successfully completed design control testing under 21CFR 820 and ISO 13485. It is considered substantially equivalent to predicate devices, with software documentation provided for a 'Major' Level of Concern. GE HealthCare consistently tops the FDA's list of AI-enabled medical device authorizations.
Pricing Model
GE HealthCare utilizes a hybrid revenue model, combining capital equipment sales with recurring services, including AI-driven predictive analytics subscriptions and software licensing. Subscription-based models are embraced, offering a pay-as-you-go approach with automatic upgrades and predictable budgeting. Specific pricing for Auto Segmentation can be requested directly from GE HealthCare.
User Experience
Aims to empower clinicians, reduce cognitive burden, and enable smarter, more efficient workflows. Features intuitive and user-friendly interfaces, flexible viewing, and reporting tools to simplify review processes. AI tools are designed to minimize burnout and discomfort by reducing physical and cognitive demands, with features like SmartEdit allowing for easy adjustments.
Support Quality
GE HealthCare is a trusted global healthcare solutions provider committed to responsible AI development, ensuring proper human oversight. Under subscription models, the company is incentivized to provide excellent support, care, and uptime. They offer services to help customers effectively utilize AI capabilities.
Implementation Complexity
Designed for seamless integration into existing clinical workflows. Tools like App Orchestrator aim to reduce complexity and risks associated with interfacing multiple systems. The platform supports both cloud and server-based deployments. While historically complex, deep learning advancements have made auto-segmentation more accessible.
Evidence Base
Utilizes deep learning algorithms trained on large datasets (e.g., 911 CT exams for OAR segmentation, 1.2 million anonymized images for X-ray foundation model). Performance is validated against predicate devices and shown to be consistent across various imaging parameters and patient demographics. Studies demonstrate significant time savings (e.g., up to 65% faster measurements in ultrasound) and improved accuracy (e.g., >90% segmentation accuracy for SonoSAMTrack). GE HealthCare invests approximately $1 billion annually in R&D and collaborates with academic institutions.
Physician Tip

When considering GE HealthCare's Auto Segmentation tools, physicians should focus on how these AI solutions integrate into their specific clinical workflows, particularly for radiation therapy planning and ultrasound diagnostics. Verify the tool's accuracy and consistency for your patient population and specific anatomical regions, as human oversight and editing of AI-generated contours remain crucial. Leverage the potential for reduced cognitive burden and accelerated workflows to free up time for complex cases and direct patient interaction. Explore the evidence base and compliance status to ensure alignment with clinical standards and regulatory requirements. For ultrasound applications, assess how auto-measurement and lesion segmentation features can enhance diagnostic confidence and reduce variability in your practice.

GE HealthCare emphasizes robust integration capabilities, offering seamless incorporation into existing RT workflows and interoperability with various third-party systems, including EMRs, OIS, and other AI applications via platforms like App Orchestrator. This vendor-neutral approach aims to reduce implementation complexity and facilitate a cohesive digital ecosystem. When planning deployment, evaluate the compatibility with your current infrastructure and consider the benefits of cloud-based options for scalability and updates. Ensure that data flow and communication protocols (e.g., DICOM, FHIR) are well-supported to maximize the utility of auto-segmented outputs in your review workstations and treatment planning systems.

Details

Category Oncology AI, Radiology & Imaging AI
Pricing Contact for pricing — Not publicly available; enterprise solution
DeploymentOn-premise (via Edison HealthLink)
Compliance
BAA AvailableUnknown AI-estimated
HIPAA CompliantUnknown AI-estimated
FDA Status 1 AI-estimated

Cleared by the U.S. FDA under 510(k) K230082 on May 4, 2023, as a radiological image processing software for radiation therapy. It is intended as a workflow tool for initial anatomy segmentation of organs at risk on CT images as an aid in radiation therapy planning after user confirmation.
Integrations
EHR Not specified
Specialties Oncology, Radiology

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Press & Coverage

GE HealthCare Newsroom
GE HealthCare debuts AI-supported solution, designed to improve and shorten the radiation therapy workflow, at ASTRO 2025
GE HealthCare announced updates to its Intelligent Radiation Therapy (iRT) software, featuring AI-supported auto-segmentation of organs at risk using MR Contour DL for MRI and compatibility with third-party tools for CT imaging, aiming to reduce the time from diagnosis to treatment.
2025-09
24x7mag
GE HealthCare Debuts AI-Supported Solution for Radiation Therapy Workflows
GE HealthCare's updated Intelligent Radiation Therapy (iRT) software includes AI for auto-segmentation of organs at risk using MR Contour DL for MRI, and is compatible with third-party tools for CT imaging, enhancing connectivity and streamlining workflows.
2025-09
Investing.com
GE HealthCare updates radiation therapy software to speed cancer treatment
GE HealthCare announced updates to its Intelligent Radiation Therapy (iRT) software, which now includes AI-supported auto-segmentation of organs at risk, aiming to significantly reduce the time between cancer diagnosis and treatment.
2025-09
GE HealthCare Newsroom
GE HealthCare Introduces Innovations to Improve Radiation Therapy Efficiency and Precision Care at the European Society for Therapeutic Radiology and Oncology 2023 Congress
GE HealthCare introduced Auto Segmentation, a deep learning, algorithm-based application that automatically contours and labels organs at risk from CT images to accelerate and standardize radiation therapy planning.
2023-05
GE HealthCare (United States)
Auto Segmentation | GE HealthCare (United States)
GE HealthCare's Auto Segmentation is a deep learning, algorithm-based application designed to automate the contouring of organs at risk (OARs) from CT images for radiotherapy planning, aiming to accelerate workflows and drive consistency.
unknown
accessdata.fda.gov
GE Medical Systems, LLC May 4, 2023 Niki Mavrodieva Regulatory Affairs Leader 3000 N. Grandview Blvd. WAUKESHA WI 53188 Re - accessdata.fda.gov
This FDA 510(k) clearance document details GE HealthCare's Auto Segmentation, a post-processing software that automatically generates contours of organs at risk from CT images for radiation therapy planning, intended as a workflow tool.
2023-05
Frontiers in Oncology
Comprehensive deep learning-based framework for automatic organs-at-risk segmentation in head-and-neck and pelvis for MR-guided radiation therapy planning - Frontiers
This peer-reviewed article discusses a deep learning-based framework for automatic segmentation of organs-at-risk in head-and-neck and pelvis using T2-weighted MR images, with several authors employed by GE HealthCare.
2023-09
ICE Magazine
GE HealthCare to Spotlight AI-enabled Solutions at ESTRO 2026 - ICE Magazine
GE HealthCare will highlight AI-enabled solutions at ESTRO 2026, including MIM SurePlan MRT with deep learning-based auto segmentation for molecular radiotherapy and MIM Contour Protu00e9gu00e9AI+ for AI-driven auto-contouring in radiation therapy planning.
2026-05

Videos

Product demos, reviews, and walkthroughs for Auto Segmentation.

View all on YouTube

Frequently Asked Questions

Auto-segmentation typically integrates as a post-acquisition processing step, often within Picture Archiving and Communication Systems (PACS) or dedicated workstations. It automatically delineates structures of interest on medical images, providing a preliminary segmentation that physicians can review and edit. This aims to streamline image analysis and reduce manual effort, allowing physicians to focus more on diagnostics and decision-making.
For clinical use, auto-segmentation software must typically have regulatory clearance or approval from bodies like the FDA in the United States or CE marking in Europe. Physicians should verify these approvals to ensure the software meets safety and efficacy standards for medical devices. Several AI-driven segmentation platforms have already received FDA clearance for various applications, including orthopedic trauma, maxillofacial, cardiovascular, and PET scans.
Reputable auto-segmentation solutions are designed with robust security features, including encryption, access controls, and anonymization capabilities, to protect patient health information (PHI). Providers should ensure the vendor adheres to HIPAA regulations and has appropriate Business Associate Agreements (BAAs) in place. Many tools are built with HIPAA-compliant infrastructure and can operate within secure, on-premise, or cloud environments to prevent data leaks.
The main alternative is manual segmentation, where a physician or trained technician meticulously outlines structures on images, which is often time-consuming and subjective. Other alternatives include semi-automated tools that require significant user input, or traditional image processing algorithms that are not AI-driven and may be less adaptable or accurate. Hybrid approaches combining traditional methods with deep learning are also gaining momentum.
Pricing models vary, often including subscription fees, pay-per-use models, or custom/tiered pricing. The cost can range significantly, from tens of thousands to hundreds of thousands of dollars, depending on the complexity, features, and integration needs. While specific reimbursement codes for the *use* of AI auto-segmentation are still evolving, some AI services in radiology are being reimbursed, and New Technology Add-on Payments (NTAPs) can offer temporary reimbursement in inpatient settings.
Limitations include potential inaccuracies with atypical anatomies, artifacts, or rare pathologies, requiring physician oversight and manual correction. Over-reliance can lead to diagnostic errors if the output is not thoroughly reviewed, and the 'black box' nature of some AI models can make it challenging to understand their decision-making. Data bias in training datasets can also lead to failures, highlighting the need for diverse and representative data.

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Suggest an Edit → | Last Verified: 2026-04-18 | First Added: 2026-04-18
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