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Cited by 216Medical Image Segmentation TechniquesBrain Tumor Detection and ClassificationAdvanced Radiotherapy Techniques

Brain tumor target volume determination for radiation treatment planning through automated MRI segmentation

Mazzara, Gloria P, Velthuizen, Robert P, Pearlman, James L, Greenberg, Harvey M, Wagner, Henry·2004·International Journal of Radiation Oncology*Biology*Physics
DOI 10.1016/j.ijrobp.2004.01.026
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Abstract

Purpose To assess the effectiveness of two automated magnetic resonance imaging (MRI) segmentation methods in determining the gross tumor volume (GTV) of brain tumors for use in radiation therapy treatment planning.Methods and materials Two automated MRI tumor segmentation methods (supervised k-nearest neighbors [kNN] and automatic knowledge-guided [KG]) were evaluated for their potential as "cyber colleagues." This required an initial determination of the accuracy and variability of radiation oncologists engaged in the manual definition of the GTV in MRI registered with computed tomography images for 11 glioma patients. Three sets of contours were defined for each of these patients by three radiation oncologists. These outlines were compared directly to establish inter- and intraoperator variability among the radiation oncologists. A novel, probabilistic measurement of accuracy was introduced to compare the level of agreement among the automated MRI segmentations. The accuracy was determined by comparing the volumes obtained by the automated segmentation methods with the weighted average volumes prepared by the radiation oncologists.Results Intra- and inter-operator variability in outlining was found to be an average of 20% +/- 15% and 28% +/- 12%, respectively. Lowest intraoperator variability was found for the physician who spent the most time producing the contours. The average accuracy of the kNN segmentation method was 56% +/- 6% for all 11 cases, whereas that of the KG method was 52% +/- 7% for 7 of the 11 cases when compared with the physician contours. For the areas of the contours where the oncologists were in substantial agreement (i.e., the center of the tumor volume), the accuracy of kNN and KG was 75% and 72%, respectively. The automated segmentation methods were found to be least accurate in outlining at the edges of the tumor volume.Conclusions The kNN method was able to segment all cases, whereas the KG method was limited to enhancing tumors and gliomas with clear enhancing edges and no cystic formation. Both methods undersegment the tumor volume when compared with the radiation oncologists and performed within the variability of the contouring performed by experienced radiation oncologists based on the same data.

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  • The preliminary federation model was already within the intra- and inter-rater variability range for this use-case (20% and 28%, respectively), so any further improvements for the full federation consensus model would be expected to be minimal.

    “Further noting that the preliminary federation model was already within the intra- and inter-rater variability range for this use-case (20% and 28%, respectively)47, any further improvements for the full federation consensus model would be expected to be minimal35–38.”

    From Federated learning enables big data for rare cancer boundary detection (2022) · cites this paper as [47]

  • Manual segmentation of brain tumor images has intra-expert variabilities of 20 ± 15% and inter-expert variabilities of 28 ± 12%.

    “In their work, Mazzara et al. [11] reported intra-expert variabilities of 20 ± 15% and inter-experts variabilities of 28 ± 12% for manual segmentations of brain tumor images.”

    From Deep learning for brain MRI segmentation: state of the art and future directions (2017) · cites this paper as [11]

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Authors
Mazzara, G. P., Velthuizen, R. P., Pearlman, J. L., Greenberg, H. M., & Wagner, H.
Affiliations
  • University of South Florida (US)
  • Unilever (United States) (US)
  • Moffitt Cancer Center (US)
Corresponding author
Mazzara, Gloria P
Venue
International Journal of Radiation Oncology*Biology*Physics
Publisher
Elsevier BV
Published
2004-05
Volume
59
Issue
1
Pages
300-312
ISSN
0360-3016 (ISSN-L)
DOI
10.1016/j.ijrobp.2004.01.026
OpenAlex
W2167878392
Cited by
216
References
27
Citations by year
2026: 6 · 2025: 4 · 2024: 12 · 2023: 14 · 2022: 16 · 2021: 18 · 2020: 16 · 2019: 10 · 2018: 15 · 2017: 12 · 2016: 19 · 2015: 11 · 2014: 11 · 2013: 13 · 2012: 3
FWCI
4.11
Type
journal article
Language
en
Open access
closed
Licence
www.elsevier.com/tdm/userlicense/1.0/ (tdm)
Locations
  • International Journal of Radiation Oncology*Biology*Physics (publishedVersion)
  • PubMed (publishedVersion)
Topics
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
  • Life Sciences › Neuroscience › Neurology › Brain Tumor Detection and Classification
  • Physical Sciences › Physics and Astronomy › Radiation › Advanced Radiotherapy Techniques
Keywords
brain tumors, gross tumor volume, MRI segmentation, automatic contouring, k-nearest neighbors, glioma, radiotherapy treatment planning, contour accuracy, CT-MRI registration
MeSH terms
Aged, Aged, 80 and over, Algorithms, Brain Neoplasms, False Positive Reactions, Female, Glioma, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Male, Middle Aged, Radiotherapy Planning, Computer-Assisted, Time Factors, Tomography, X-Ray Computed, Reproducibility of Results, Observer Variation, Radiation Oncology, Imaging, Three-Dimensional

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