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.
Why AI-based autocontouring?
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.
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 RT 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.
Courtesy of University Hospital Groningen, Netherlands
The quality of autocontouring depends on the quality of the CT images
- Suboptimal image quality leads to suboptimal autocontouring2,3 that needs to be modified or re-done
- Radiation oncology professionals have no consistent starting point for RT planning
The value of AI-based autocontouring
Deep learning-trained organs-at-risk contouring for increased quality and consistency
Improve workflow efficiency to free up resources from routine delineating tasks
Seamless integration into the daily treatment planning workflow
Autocontouring solutions for various clinical environments
How can we accelarate the path to treatment?
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
Características e Benefícios
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.
Learn more about the benefits of AI-Rad Companion Organs RT in clinical usage from Dr. Firas Mourtada10
Depoimentos de clientes
Hear what our customers say10:
The use of AI-Rad Companion Organs RT makes our life easier. Especially the contouring of organs in the upper abdomen leads to a noteworthy reduction of turnaround time.
University Hospital Basel, Switzerland
University Hospital Basel, Switzerland
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
Rendering is based on research results that are not commercially available. Future availability cannot be guaranteed.
AI-Rad Companion Organs RT and Eclipse are two independent medical devices having individual intended purposes and must/should not considered as a system.
The case evaluation was conducted with Organs RT on syngo.via RT Image Suite.
The statements by Siemens Healthineers’ customers described herein are based on results that were achieved in the customer's unique setting. Because there is no “typical” hospital or laboratory and many variables exist (e.g., hospital size, samples mix, case mix, level of IT and/or automation adoption) there can be no guarantee that other customers will achieve the same results.
Dr. Mourtada is engaged in a collaboration with Siemens Healthineers.
Dr. Alexandros Papchristofilou is employed by an institution that receives financial support from Siemens Healthineers for collaborations.
Dr. Manuel Algara López is engaged in a collaboration with Siemens Healthineers.
Stephane Muraro is engaged in a collaboration with Siemens Healthineers.
Prof. Dr. Oliver Ott is employed by an institution that receives financial support from Siemens Healthineers for collaborations.
Dr. Christian Grehn is employed by an institution that receives financial support from Siemens Healthineers for collaboration