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Deep learning-based autocontouring
Organs-at-risk contouring in radiation therapy for various clinical environmentsAccurate 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 radiation therapy 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.
Learn more about automated organs-at-risk contouring solutions in radiation therapy for various clinical environments.
With AI-Rad Companion Trial Light1 you can easily experience the quality of our algorithms – directly via your browser and without any installation effort.
Why AI-based autocontouring?
Our deep learning-based autocontouring solutions enable precise organs-at-risk contouring in radiation therapy. They provide consistent results as a starting point for treatment:
>95% of the contouring results are clinically usable or require minor edits2.
Challenges of manual organs-at-risk contouring
Organs-at-risk (OAR) contouring creates a substantial amount of effort and is a major source of variability in radiation therapy planning. Modern treatment techniques rely on consistent OAR contours as a starting point. The time spent on OAR contouring keeps staff from focusing on clinical tasks like devising an optimal treatment plan.
Uncertain, inefficient, inconsistent
The quality of autocontouring depends on the quality of the CT images
Uncertain, inefficient, inconsistent
The quality of autocontouring depends on the quality of the CT images
Uncertain, inefficient, inconsistent
The value of AI-based autocontouring
Consistency
Deep learning-trained organs-at-risk contouring for increased quality and consistency in radiation oncology.
Courtesy of Universitätsklinikum Erlangen, Germany
Efficiency8
Improve workflow efficiency to free up resources from routine delineating tasks in radiation oncology.
Courtesy of Radiologische Allianz Hamburg, Germany
Accessibility
Seamless integration into the daily radiation oncology treatment planning workflow.
Eclipse version 17.0 was used to display the autocontouring results.
Autocontouring solutions for various clinical environments
AI-Rad Companion Organs RT now also supports MR-based organs-at-risk for male pelvis4.
Clinical outcomes
Evaluation of AI-Rad Companion Organs RT at Centre de Cancérologie du Grand Montpellier
Customer feedback Universitätsklinikum Erlangen
Listen how our AI-based autocontouring solution adds value to the clinical workflow of the Department of radiation oncology at Universitätsklinikum Erlangen.
70% time saving per OAR
Evaluation of AI-Rad Companion Organs RT at Centre de Cancérologie du Grand Montpellier
Customer feedback Universitätsklinikum Erlangen
Listen how our AI-based autocontouring solution adds value to the clinical workflow of the Department of radiation oncology at Universitätsklinikum Erlangen.
70% time saving per OAR
Evaluation of AI-Rad Companion Organs RT at Centre de Cancérologie du Grand Montpellier
Benefit: User-friendly
Benefit: Accuracy
Benefit: Consistency
Benefit: Time savings
Benefit: User-friendly
Benefit: Accuracy
Benefit: Consistency
Benefit: Time savings
Benefit: User-friendly
We follow international consensus guidelines to train our deep learning algorithms for organs-at-risk contouring:
Hear what our customers say11:
Automatic contouring helps to increase precision which is highly beneficial for patients.
Stephane Muraro
Centre de Cancérologie du Grand Montpellier (CCGM), France
In our experience, the DirectORGANS autocontouring system is very helpful and helped us especially in thoracic and abdominal tumor sites.
Prof. Dr. Oliver Ott
Department of Radiotherapy, University Hospital Erlangen, Germany
The standardized algorithms are constantly improving and deliver the same precision as an experienced radiation therapy expert - always returning consistent results. Manual contouring can vary from user to user, which affects treatment. This is not the case with this software.
Manuel Algara López, MD
Hospital del Mar, Barcelona, Spain
The benefit (of DirectORGANS) for patients is that their physicians aren't caught up with planning and having to draw each contour themselves. This means they have much more time to devote to each individual patient.
Dr. Christian Grehn
Radiation Oncologist and Co-Owner, Nordstrahl, Praxis für Strahlentherapie, Nürnberg, Germany
The term autocontouring in this context means automated contouring of organs-at-risk structures.
The products/features (mentioned herein) are not commercially available in all countries. Their future availability cannot be guaranteed.
Wu X, Udupa JK, Odhner D et al. Knowledge-Based Auto Contouring for Radiation Therapy: Object Definitions, Ground Truth Delineations, Object Quality, and Image Quality. Int J Radiat Oncol Biol Phys. 2017; 99 (2): E740
Cheung CW, Leung KY, Lam WW et al. Application of Model-based Iterative Reconstruction in Auto-contouring of Head and Neck Cases. Scientific Informal (Poster) Presentation at: LL-ROS-TH Radiation Oncology and Radiobiology Lunch Hour CME Posters; 2012 Nov 29; Chicago, IL
Radiation Oncology Incident Learning System, Aggregate Report Patient, Safety Work Product, Q4, 2017
IAEA, Radiotherapy in Cancer Care: Facing the Global Challenge, 2017
J Van der Veen, A Gulyban, S Willems, F Maes, S Nuyts, Interobserver variability in organ at risk delineation in head and neck cancer, 2021
AI-Rad Companion Organs RT and Eclipse are two independent medical devices having individual intended purposes and must/should not considered as a system.