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PET segmentation of bulky tumors: Strategies and workflows to improve inter-observer variability

BACKGROUND: PET-based tumor delineation is an error prone and labor intensive part of image analysis. Especially for patients with advanced disease showing bulky tumor FDG load, segmentations are challenging. Reducing the amount of user-interaction in the segmentation might help to facilitate segmen...

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Autores principales: Pfaehler, Elisabeth, Burggraaff, Coreline, Kramer, Gem, Zijlstra, Josée, Hoekstra, Otto S., Jalving, Mathilde, Noordzij, Walter, Brouwers, Adrienne H., Stevenson, Marc G., de Jong, Johan, Boellaard, Ronald
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7105134/
https://www.ncbi.nlm.nih.gov/pubmed/32226030
http://dx.doi.org/10.1371/journal.pone.0230901
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author Pfaehler, Elisabeth
Burggraaff, Coreline
Kramer, Gem
Zijlstra, Josée
Hoekstra, Otto S.
Jalving, Mathilde
Noordzij, Walter
Brouwers, Adrienne H.
Stevenson, Marc G.
de Jong, Johan
Boellaard, Ronald
author_facet Pfaehler, Elisabeth
Burggraaff, Coreline
Kramer, Gem
Zijlstra, Josée
Hoekstra, Otto S.
Jalving, Mathilde
Noordzij, Walter
Brouwers, Adrienne H.
Stevenson, Marc G.
de Jong, Johan
Boellaard, Ronald
author_sort Pfaehler, Elisabeth
collection PubMed
description BACKGROUND: PET-based tumor delineation is an error prone and labor intensive part of image analysis. Especially for patients with advanced disease showing bulky tumor FDG load, segmentations are challenging. Reducing the amount of user-interaction in the segmentation might help to facilitate segmentation tasks especially when labeling bulky and complex tumors. Therefore, this study reports on segmentation workflows/strategies that may reduce the inter-observer variability for large tumors with complex shapes with different levels of user-interaction. METHODS: Twenty PET images of bulky tumors were delineated independently by six observers using four strategies: (I) manual, (II) interactive threshold-based, (III) interactive threshold-based segmentation with the additional presentation of the PET-gradient image and (IV) the selection of the most reasonable result out of four established semi-automatic segmentation algorithms (Select-the-best approach). The segmentations were compared using Jaccard coefficients (JC) and percentage volume differences. To obtain a reference standard, a majority vote (MV) segmentation was calculated including all segmentations of experienced observers. Performed and MV segmentations were compared regarding positive predictive value (PPV), sensitivity (SE), and percentage volume differences. RESULTS: The results show that with decreasing user-interaction the inter-observer variability decreases. JC values and percentage volume differences of Select-the-best and a workflow including gradient information were significantly better than the measurements of the other segmentation strategies (p-value<0.01). Interactive threshold-based and manual segmentations also result in significant lower and more variable PPV/SE values when compared with the MV segmentation. CONCLUSIONS: FDG PET segmentations of bulky tumors using strategies with lower user-interaction showed less inter-observer variability. None of the methods led to good results in all cases, but use of either the gradient or the Select-the-best workflow did outperform the other strategies tested and may be a good candidate for fast and reliable labeling of bulky and heterogeneous tumors.
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spelling pubmed-71051342020-04-03 PET segmentation of bulky tumors: Strategies and workflows to improve inter-observer variability Pfaehler, Elisabeth Burggraaff, Coreline Kramer, Gem Zijlstra, Josée Hoekstra, Otto S. Jalving, Mathilde Noordzij, Walter Brouwers, Adrienne H. Stevenson, Marc G. de Jong, Johan Boellaard, Ronald PLoS One Research Article BACKGROUND: PET-based tumor delineation is an error prone and labor intensive part of image analysis. Especially for patients with advanced disease showing bulky tumor FDG load, segmentations are challenging. Reducing the amount of user-interaction in the segmentation might help to facilitate segmentation tasks especially when labeling bulky and complex tumors. Therefore, this study reports on segmentation workflows/strategies that may reduce the inter-observer variability for large tumors with complex shapes with different levels of user-interaction. METHODS: Twenty PET images of bulky tumors were delineated independently by six observers using four strategies: (I) manual, (II) interactive threshold-based, (III) interactive threshold-based segmentation with the additional presentation of the PET-gradient image and (IV) the selection of the most reasonable result out of four established semi-automatic segmentation algorithms (Select-the-best approach). The segmentations were compared using Jaccard coefficients (JC) and percentage volume differences. To obtain a reference standard, a majority vote (MV) segmentation was calculated including all segmentations of experienced observers. Performed and MV segmentations were compared regarding positive predictive value (PPV), sensitivity (SE), and percentage volume differences. RESULTS: The results show that with decreasing user-interaction the inter-observer variability decreases. JC values and percentage volume differences of Select-the-best and a workflow including gradient information were significantly better than the measurements of the other segmentation strategies (p-value<0.01). Interactive threshold-based and manual segmentations also result in significant lower and more variable PPV/SE values when compared with the MV segmentation. CONCLUSIONS: FDG PET segmentations of bulky tumors using strategies with lower user-interaction showed less inter-observer variability. None of the methods led to good results in all cases, but use of either the gradient or the Select-the-best workflow did outperform the other strategies tested and may be a good candidate for fast and reliable labeling of bulky and heterogeneous tumors. Public Library of Science 2020-03-30 /pmc/articles/PMC7105134/ /pubmed/32226030 http://dx.doi.org/10.1371/journal.pone.0230901 Text en © 2020 Pfaehler et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Pfaehler, Elisabeth
Burggraaff, Coreline
Kramer, Gem
Zijlstra, Josée
Hoekstra, Otto S.
Jalving, Mathilde
Noordzij, Walter
Brouwers, Adrienne H.
Stevenson, Marc G.
de Jong, Johan
Boellaard, Ronald
PET segmentation of bulky tumors: Strategies and workflows to improve inter-observer variability
title PET segmentation of bulky tumors: Strategies and workflows to improve inter-observer variability
title_full PET segmentation of bulky tumors: Strategies and workflows to improve inter-observer variability
title_fullStr PET segmentation of bulky tumors: Strategies and workflows to improve inter-observer variability
title_full_unstemmed PET segmentation of bulky tumors: Strategies and workflows to improve inter-observer variability
title_short PET segmentation of bulky tumors: Strategies and workflows to improve inter-observer variability
title_sort pet segmentation of bulky tumors: strategies and workflows to improve inter-observer variability
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7105134/
https://www.ncbi.nlm.nih.gov/pubmed/32226030
http://dx.doi.org/10.1371/journal.pone.0230901
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