Skip NavigationSkip to Content

Auto-segmentation for Thoracic Radiation Treatment Planning: A Grand Challenge at AAPM 2017

  1. Author:
    Yang, Jinzhong
    Veeraraghavan, Harini
    Armato, Samuel G
    Farahani, Keyvan
    Kirby, Justin
    Kalpathy-Kramer, Jayashree
    van Elmpt, Wouter
    Dekker, Andre
    Han, Xiao
    Feng, Xue
    Aljabar, Paul
    Oliveira, Bruno
    van der Heyden, Brent
    Zamdborg, Leonid
    Lam, Dao
    Gooding, Mark
    Sharp, Gregory C
  2. Author Address

    Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA., Memorial Sloan Kettering Cancer Center, New York, NY, USA., Department of Radiology, The University of Chicago, Chicago, IL, USA., Cancer Imaging Program, National Cancer Institute, Bethesda, MD, USA., Cancer Imaging Program, Frederick National Laboratory for Cancer Research sponsored by the National Cancer Institute, Frederick, MD, USA., Harvard Medical School, Boston, MA, USA., Massachusetts General Hospital, Boston, MA, USA., Department of Radiation Oncology (MAASTRO) GROW - School for Oncology and Developmental Biology, Maastricht University Medical Center, Maastricht, The Netherlands., Elekta Inc Maryland, Heights, MO, USA., Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, USA., Mirada Medical Ltd, Oxford, UK., Life and Health Sciences Research Institute (ICVS)School of Medicine, University of Minho, Braga, Portugal., ICVS/3Bs - PT Government Associaste Laboratory, Braga/Guimares, Portugal., Department of Radiation Oncology, Beaumont Health, Royal Oak, MI, USA., Department of Radiation Oncology, Washington University School of Medicine in St. Louis, St. Louis, MO, USA.,
    1. Year: 2018
    2. Date: Oct
    3. Epub Date: 2018 08 24
  1. Journal: Medical Physics
    1. 45
    2. 10
    3. Pages: 4568-4581
  2. Type of Article: Article
  3. ISSN: 0094-2405
  1. Abstract:

    This report presents the methods and results of the Thoracic Auto-Segmentation Challenge organized at the 2017 Annual Meeting of American Association of Physicists in Medicine. The purpose of the challenge was to provide a benchmark dataset and platform for evaluating performance of auto-segmentation methods of organs at risk (OARs) in thoracic CT images. Sixty thoracic CT scans provided by three different institutions were separated into 36 training, 12 offline testing, and 12 online testing scans. Eleven participants completed the offline challenge, and seven completed the online challenge. The OARs were left and right lungs, heart, esophagus and spinal cord. Clinical contours used for treatment planning were quality checked and edited to adhere to the RTOG 1106 contouring guidelines. Algorithms were evaluated using the Dice coefficient, Hausdorff distance, and mean surface distance. A consolidated score was computed by normalizing the metrics against inter-rater variability and averaging over all patients and structures. The inter-rater study revealed highest variability in Dice for the esophagus and spinal cord, and in surface distances for lungs and heart. Five out of seven algorithms that participated in the online challenge employed deep learning methods. Although the top three participants using deep learning produced the best segmentation for all structures, there was no significant difference in the performance among them. The fourth place participant used a multi-atlas based approach. The highest Dice scores were produced for lungs, with averages ranging from 0.95-0.98, while the lowest Dice scores were produced for esophagus, with a range of 0.55-0.72. The results of the challenge showed that the lungs and heart can be segmented fairly accurately by various algorithms, while deep learning methods performed better on the esophagus. Our dataset together with the manual contours for all training cases continues to be available publicly as an ongoing benchmarking resource. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

    See More

External Sources

  1. DOI: 10.1002/mp.13141
  2. PMID: 30144101
  3. WOS: 000446995000038

Library Notes

  1. Fiscal Year: FY2017-2018
NCI at Frederick

You are leaving a government website.

This external link provides additional information that is consistent with the intended purpose of this site. The government cannot attest to the accuracy of a non-federal site.

Linking to a non-federal site does not constitute an endorsement by this institution or any of its employees of the sponsors or the information and products presented on the site. You will be subject to the destination site's privacy policy when you follow the link.

ContinueCancel