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Deep learning-based autocontouring

Organs-at-risk contouring in Radiation Therapy for various clinical environments

Accurate contouring of organs-at-risk (OAR) is one of the major bottlenecks of Radiation Therapy planning, but still the necessary first step in the process. Therefore, the increase in the number of patients puts significant pressure on radiotherapy staff responsible for consistent OAR contouring results. Advances in technology and artificial intelligence can help automate repetitive tasks such as OAR contouring, thus reduce workload, and standardize key CT simulation steps.

Speak to an expert and learn more about automated contouring solutions for various radiation therapy clinical environments.

Clinical Use

Our deep-learning based autocontouring solutions enable precise organs-at-risk contouring. They provide consistent results as a starting point for treatment: 

>95% of the contouring results are clinically usable or require minor edits1.

The value of AI-based autocontouring

deep-learning based autocontouring

Consistency

Deep learning-trained organs-at-risk contouring for increased quality and consistency


Courtesy of Radiologische Allianz Hamburg, Germany

autocontouring radiation therapy

Efficiency7

Improve workflow efficiency to free up resources from routine delineating tasks 


Courtesy of Radiologische Allianz Hamburg, Germany

artificial intelligence in radiation therapy

Accessibility

Seamless integration into the daily treatment planning workflow


Courtesy of Radiologische Allianz Hamburg, Germany

AI-based autocontouring: Efficiency and automation throughout the radiation therapy planning process.

With AI-based autocontouring and EclipseTM we enable you to reach efficiency and automation throughout the planning process. The contouring of the organs at risk is done automatically with the support of deep learning algorithms. It may reduce unwarranted variations with high-quality contours that approach the level of consensus-based contours.8

Features & Benefits

Testimonials

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