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Research ArticleAI/Advanced Image Analysis
Open Access

Need for Objective Task-Based Evaluation of Image Segmentation Algorithms for Quantitative PET: A Study with ACRIN 6668/RTOG 0235 Multicenter Clinical Trial Data

Ziping Liu, Joyce C. Mhlanga, Huitian Xia, Barry A. Siegel and Abhinav K. Jha
Journal of Nuclear Medicine March 2024, 65 (3) 485-492; DOI: https://doi.org/10.2967/jnumed.123.266018
Ziping Liu
1Department of Biomedical Engineering, Washington University, St. Louis, Missouri;
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Joyce C. Mhlanga
2Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, Missouri; and
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Huitian Xia
1Department of Biomedical Engineering, Washington University, St. Louis, Missouri;
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Barry A. Siegel
2Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, Missouri; and
3Alvin J. Siteman Cancer Center, Washington University School of Medicine, St. Louis, Missouri
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Abhinav K. Jha
1Department of Biomedical Engineering, Washington University, St. Louis, Missouri;
2Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, Missouri; and
3Alvin J. Siteman Cancer Center, Washington University School of Medicine, St. Louis, Missouri
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Article Information

vol. 65 no. 3 485-492
DOI 
https://doi.org/10.2967/jnumed.123.266018
PubMed 
38360049

Published By 
Society of Nuclear Medicine
Print ISSN 
0161-5505
Online ISSN 
2159-662X
History 
  • Received for publication May 12, 2023
  • Accepted for publication December 19, 2023
  • Published online March 1, 2024.

Article Versions

  • previous version (February 15, 2024 - 08:41).
  • You are viewing the most recent version of this article.
Copyright & Usage 
© 2024 by the Society of Nuclear Medicine and Molecular Imaging. Immediate Open Access: Creative Commons Attribution 4.0 International License (CC BY) allows users to share and adapt with attribution, excluding materials credited to previous publications. License: https://creativecommons.org/licenses/by/4.0/. Details: http://jnm.snmjournals.org/site/misc/permission.xhtml.

Author Information

  1. Ziping Liu1,
  2. Joyce C. Mhlanga2,
  3. Huitian Xia1,
  4. Barry A. Siegel2,3 and
  5. Abhinav K. Jha1,2,3
  1. 1Department of Biomedical Engineering, Washington University, St. Louis, Missouri;
  2. 2Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, Missouri; and
  3. 3Alvin J. Siteman Cancer Center, Washington University School of Medicine, St. Louis, Missouri
  1. For correspondence or reprints, contact Abhinav K. Jha (a.jha{at}wustl.edu).
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Journal of Nuclear Medicine: 65 (3)
Journal of Nuclear Medicine
Vol. 65, Issue 3
March 1, 2024
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Need for Objective Task-Based Evaluation of Image Segmentation Algorithms for Quantitative PET: A Study with ACRIN 6668/RTOG 0235 Multicenter Clinical Trial Data
Ziping Liu, Joyce C. Mhlanga, Huitian Xia, Barry A. Siegel, Abhinav K. Jha
Journal of Nuclear Medicine Mar 2024, 65 (3) 485-492; DOI: 10.2967/jnumed.123.266018

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Need for Objective Task-Based Evaluation of Image Segmentation Algorithms for Quantitative PET: A Study with ACRIN 6668/RTOG 0235 Multicenter Clinical Trial Data
Ziping Liu, Joyce C. Mhlanga, Huitian Xia, Barry A. Siegel, Abhinav K. Jha
Journal of Nuclear Medicine Mar 2024, 65 (3) 485-492; DOI: 10.2967/jnumed.123.266018
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  • The Use of Maximum-Intensity Projections and Deep Learning Adds Value to the Fully Automatic Segmentation of Lesions Avid for [18F]FDG and [68Ga]Ga-PSMA in PET/CT
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Keywords

  • task-based evaluation
  • multicenter clinical trial
  • segmentation
  • quantitative imaging
  • deep learning
  • artificial intelligence
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