Evaluation of Automatic Atlas-Based Lymph Node Segmentation for Head-and-Neck Cancer

Stapleford, Liza J; Lawson, Joshua D; Perkins, Charles; Edelman, Scott; Davis, Lawrence

doi:10.1016/j.ijrobp.2009.09.023

Title: Evaluation of Automatic Atlas-Based Lymph Node Segmentation for Head-and-Neck Cancer

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Abstract

Purpose: To evaluate if automatic atlas-based lymph node segmentation (LNS) improves efficiency and decreases inter-observer variability while maintaining accuracy. Methods and Materials: Five physicians with head-and-neck IMRT experience used computed tomography (CT) data from 5 patients to create bilateral neck clinical target volumes covering specified nodal levels. A second contour set was automatically generated using a commercially available atlas. Physicians modified the automatic contours to make them acceptable for treatment planning. To assess contour variability, the Simultaneous Truth and Performance Level Estimation (STAPLE) algorithm was used to take collections of contours and calculate a probabilistic estimate of the 'true' segmentation. Differences between the manual, automatic, and automatic-modified (AM) contours were analyzed using multiple metrics. Results: Compared with the 'true' segmentation created from manual contours, the automatic contours had a high degree of accuracy, with sensitivity, Dice similarity coefficient, and mean/max surface disagreement values comparable to the average manual contour (86%, 76%, 3.3/17.4 mm automatic vs. 73%, 79%, 2.8/17 mm manual). The AM group was more consistent than the manual group for multiple metrics, most notably reducing the range of contour volume (106-430 mL manual vs. 176-347 mL AM) and percent false positivity (1-37% manual vs. 1-7% AM). Average contouring timemore »« less

Authors:

Stapleford, Liza J ^[1]; Lawson, Joshua D ^[1]; Perkins, Charles; Edelman, Scott; Davis, Lawrence ^[1]

Department of Radiation Oncology, Emory University School of Medicine and Winship Cancer Institute of Emory University Atlanta, GA (United States)

Publication Date:: Thu Jul 01 00:00:00 EDT 2010

OSTI Identifier:: 21436059

Resource Type:: Journal Article

Journal Name:: International Journal of Radiation Oncology, Biology and Physics

Additional Journal Information:: Journal Volume: 77; Journal Issue: 3; Other Information: DOI: 10.1016/j.ijrobp.2009.09.023; PII: S0360-3016 (09)03217-9; Copyright (c) 2010 Elsevier Science B.V., Amsterdam, The Netherlands, All rights reserved.; Journal ID: ISSN 0360-3016

Country of Publication:: United States

Language:: English

Subject:: 62 RADIOLOGY AND NUCLEAR MEDICINE; ALGORITHMS; HEAD; LYMPH NODES; NECK; NEOPLASMS; PROBABILISTIC ESTIMATION; RADIOTHERAPY; BODY; CALCULATION METHODS; DISEASES; LYMPHATIC SYSTEM; MATHEMATICAL LOGIC; MEDICINE; NUCLEAR MEDICINE; RADIOLOGY; THERAPY

Citation Formats


                    Stapleford, Liza J, Lawson, Joshua D, Department of Radiation Oncology, University of California, San Diego, School of Medicine, La Jolla, CA, Perkins, Charles, Edelman, Scott, and Davis, Lawrence. Evaluation of Automatic Atlas-Based Lymph Node Segmentation for Head-and-Neck Cancer.  United States: N. p., 2010. 
        Web.  doi:10.1016/j.ijrobp.2009.09.023.

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                    Stapleford, Liza J, Lawson, Joshua D, Department of Radiation Oncology, University of California, San Diego, School of Medicine, La Jolla, CA, Perkins, Charles, Edelman, Scott, & Davis, Lawrence. Evaluation of Automatic Atlas-Based Lymph Node Segmentation for Head-and-Neck Cancer.  United States.  https://doi.org/10.1016/j.ijrobp.2009.09.023

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                    Stapleford, Liza J, Lawson, Joshua D, Department of Radiation Oncology, University of California, San Diego, School of Medicine, La Jolla, CA, Perkins, Charles, Edelman, Scott, and Davis, Lawrence. 2010.  
        "Evaluation of Automatic Atlas-Based Lymph Node Segmentation for Head-and-Neck Cancer".  United States.  https://doi.org/10.1016/j.ijrobp.2009.09.023.

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@article{osti_21436059,

  title        = {Evaluation of Automatic Atlas-Based Lymph Node Segmentation for Head-and-Neck Cancer},

  author       = {Stapleford, Liza J and Lawson, Joshua D and Department of Radiation Oncology, University of California, San Diego, School of Medicine, La Jolla, CA and Perkins, Charles and Edelman, Scott and Davis, Lawrence},

  abstractNote = {Purpose: To evaluate if automatic atlas-based lymph node segmentation (LNS) improves efficiency and decreases inter-observer variability while maintaining accuracy. Methods and Materials: Five physicians with head-and-neck IMRT experience used computed tomography (CT) data from 5 patients to create bilateral neck clinical target volumes covering specified nodal levels. A second contour set was automatically generated using a commercially available atlas. Physicians modified the automatic contours to make them acceptable for treatment planning. To assess contour variability, the Simultaneous Truth and Performance Level Estimation (STAPLE) algorithm was used to take collections of contours and calculate a probabilistic estimate of the 'true' segmentation. Differences between the manual, automatic, and automatic-modified (AM) contours were analyzed using multiple metrics. Results: Compared with the 'true' segmentation created from manual contours, the automatic contours had a high degree of accuracy, with sensitivity, Dice similarity coefficient, and mean/max surface disagreement values comparable to the average manual contour (86%, 76%, 3.3/17.4 mm automatic vs. 73%, 79%, 2.8/17 mm manual). The AM group was more consistent than the manual group for multiple metrics, most notably reducing the range of contour volume (106-430 mL manual vs. 176-347 mL AM) and percent false positivity (1-37% manual vs. 1-7% AM). Average contouring time savings with the automatic segmentation was 11.5 min per patient, a 35% reduction. Conclusions: Using the STAPLE algorithm to generate 'true' contours from multiple physician contours, we demonstrated that, in comparison with manual segmentation, atlas-based automatic LNS for head-and-neck cancer is accurate, efficient, and reduces interobserver variability.},

  doi          = {10.1016/j.ijrobp.2009.09.023},

  url          = {https://www.osti.gov/biblio/21436059},
  journal      = {International Journal of Radiation Oncology, Biology and Physics},

  issn         = {0360-3016},

  number       = 3,

  volume       = 77,

  place        = {United States},

  year         = {Thu Jul 01 00:00:00 EDT 2010},

  month        = {Thu Jul 01 00:00:00 EDT 2010}

}

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