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Deep learning-based autocontouring
Autocontouring in radiation therapy for various clinical environments
Accurate contouring of structures, like organs at risk (OAR), and lymph nodes 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 contouring results. Advances in technology and artificial intelligence can help automate repetitive tasks such as contouring, thus reduce workload, and standardize key simulation steps.
Learn more about automated organs-at-risk and gross tumor volume (GTV) contouring solutions in radiation therapy for various clinical environments supporting multiple guidelines.
Clinical Use
Why AI-based autocontouring?
Our deep learning-based autocontouring solutions enable precise organs-at-risk contouring in radiation therapy for over 200 structures. 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.
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Courtesy of University Hospital Groningen, Netherlands
The quality of autocontouring depends on the quality of the CT images
- Suboptimal image quality leads to suboptimal autocontouring3,4 that needs to be modified or re-done
- Radiation oncology professionals have no consistent starting point for RT planning
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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
Features & Benefits
Deep-learning based autocontouring for lymph nodes8
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Courtesy of Universitätsklinikum Erlangen, Germany
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MR-based contouring to support an MR-only workflow for RT planning
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Courtesy of Leopoldina-Krankenhaus der Stadt Schweinfurt GmbH, Schweinfurt, Germany
AI-Rad Companion Organs RT and syngo.via RT Image Suite also support MR-based organs at risk for male pelvis.
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Clinical outcomes
Guidelines
We follow international consensus guidelines to train our deep learning algorithms for organs-at-risk contouring:
- Brainstem14: Brouwer et al. 2015, DAHANCA 2020
- Head & neck OAR: Brouwer et al., 2015
- Head & neck lymph nodes: Gregoire et al., 2014
- Glottis and supraglottic larynx14: Brouwer et al. 2015, DAHANCA 2020
- Esophagus14: RTOG guideline, Kong et al., 2011, DAHANCA 2020
- Breast, left and right14: ESTRO guideline, Offersen et al., 2015, RTOG guideline, Kong et al., 2011
- Thoracic OAR: RTOG guideline, Kong et al., 2011
- Thoracic lymph nodes: IASLC Rusch et al. 2009
- Brain for MR: Eekers et al. 2021
- Male pelvis for MR: RTOG Gay et al. 2012
- Abdomen OAR: CT Anatomy for Radiotherapy, Second edition, Pete Bridge and David J Tipper, M&K Publishing
- Pelvic OAR: RTOG guideline, Gay et al., 2012
- Pelvic lymph nodes: RTOG guideline, Lawton et al., 2009
Testimonials
Hear what our customers say12:
The AI software completed the contouring for each patient in just one to two minutes, reducing the workload by an average of 75%, and in some cases saving over 25 minutes per patient. “We still needed to review the AI contours, but the correction time was only three to four minutes,” says Tezcanli. “One case required nine minutes, but even with that patient we had a time saving of 75%.”
Professor of radiation oncology at Acibadem Atasehir Hospital in Turkey
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.
1
For demo purposes only. Please note that AI-Rad Companion Trial Light may not provide the full functionality of the released medical device.
2
The case evaluation was conducted with AI-Rad Companion Organs RT. Clinical data provided by Hopsital Montpellier. The results by Siemens Healthineers' customer described herein are based on results that were achieved in the customers' unique setting. Since there is no "typical" hospital and many variables exist (e.g., hospital size, case mix, level of IT adoption) there can be no guarantee that other customers will achieve the same results.
3
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
4
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
5
Radiation Oncology Incident Learning System, Aggregate Report Patient, Safety Work Product, Q4, 2017
6
IAEA, Radiotherapy in Cancer Care: Facing the Global Challenge, 2017
7
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
8
Rendering is based on research results that are not commercially available. Future availability cannot be guaranteed.
9
AI-Rad Companion Organs RT VA70 is pending 510(k) clearance and is not yet commercially available in the United States.
10
AI-Rad Companion Organs RT and Eclipse are two independent medical devices having individual intended purposes and must/should not considered as a system.
11
The case evaluation was conducted with Organs RT on syngo.via RT Image Suite.
12
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, Dr. Alexandros Papchristofilou, Dr. Manuel Algara López, Stephane Muraro, Dr. Olivier Lauche, Prof. Dr. Oliver Ott, and Dr. Christian Grehn are employed by institutions that receive financial support from Siemens Healthineers for collaborations.
Dr. Mourtada, Dr. Alexandros Papchristofilou, Dr. Manuel Algara López, Stephane Muraro, Dr. Olivier Lauche, Prof. Dr. Oliver Ott, and Dr. Christian Grehn are employed by institutions that receive financial support from Siemens Healthineers for collaborations.
13
syngo.via RT Image Suite VC10 is pending 510(k) clearance and is not yet commercially available in the United States.
14
Applied guideline is configurable.