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Analyzing human decisions in IGRT of head-and-neck cancer patients to teach image registration algorithms what experts know
BACKGROUND: In IGRT of deformable head-and-neck anatomy, patient setup corrections are derived by rigid registration methods. In practice, experienced radiation therapists often correct the resulting vectors, thus indicating a different prioritization of alignment of local structures. Purpose of thi...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5480194/ https://www.ncbi.nlm.nih.gov/pubmed/28637483 http://dx.doi.org/10.1186/s13014-017-0842-8 |
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author | Stoiber, Eva Maria Bougatf, Nina Teske, Hendrik Bierstedt, Christian Oetzel, Dieter Debus, Jürgen Bendl, Rolf Giske, Kristina |
author_facet | Stoiber, Eva Maria Bougatf, Nina Teske, Hendrik Bierstedt, Christian Oetzel, Dieter Debus, Jürgen Bendl, Rolf Giske, Kristina |
author_sort | Stoiber, Eva Maria |
collection | PubMed |
description | BACKGROUND: In IGRT of deformable head-and-neck anatomy, patient setup corrections are derived by rigid registration methods. In practice, experienced radiation therapists often correct the resulting vectors, thus indicating a different prioritization of alignment of local structures. Purpose of this study is to transfer the knowledge experts apply when correcting the automatically generated result (pre-match) to automated registration. METHODS: Datasets of 25 head-and-neck-cancer patients with daily CBCTs and corresponding approved setup correction vectors were analyzed. Local similarity measures were evaluated to identify the criteria for human corrections with regard to alignment quality, analogous to the radiomics approach. Clustering of similarity improvement patterns is applied to reveal priorities in the alignment quality. RESULTS: The radiation therapists prioritized to align the spinal cord closest to the high-dose area. Both target volumes followed with second and third highest priority. The bony pre-match influenced the human correction along the crania-caudal axis. Based on the extracted priorities, a new rigid registration procedure is constructed which is capable of reproducing the corrections of experts. CONCLUSIONS: The proposed approach extracts knowledge of experts performing IGRT corrections to enable new rigid registration methods that are capable of mimicking human decisions. In the future, the deduction of knowledge-based corrections for different cohorts can be established automating such supervised learning approaches. |
format | Online Article Text |
id | pubmed-5480194 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-54801942017-06-23 Analyzing human decisions in IGRT of head-and-neck cancer patients to teach image registration algorithms what experts know Stoiber, Eva Maria Bougatf, Nina Teske, Hendrik Bierstedt, Christian Oetzel, Dieter Debus, Jürgen Bendl, Rolf Giske, Kristina Radiat Oncol Research BACKGROUND: In IGRT of deformable head-and-neck anatomy, patient setup corrections are derived by rigid registration methods. In practice, experienced radiation therapists often correct the resulting vectors, thus indicating a different prioritization of alignment of local structures. Purpose of this study is to transfer the knowledge experts apply when correcting the automatically generated result (pre-match) to automated registration. METHODS: Datasets of 25 head-and-neck-cancer patients with daily CBCTs and corresponding approved setup correction vectors were analyzed. Local similarity measures were evaluated to identify the criteria for human corrections with regard to alignment quality, analogous to the radiomics approach. Clustering of similarity improvement patterns is applied to reveal priorities in the alignment quality. RESULTS: The radiation therapists prioritized to align the spinal cord closest to the high-dose area. Both target volumes followed with second and third highest priority. The bony pre-match influenced the human correction along the crania-caudal axis. Based on the extracted priorities, a new rigid registration procedure is constructed which is capable of reproducing the corrections of experts. CONCLUSIONS: The proposed approach extracts knowledge of experts performing IGRT corrections to enable new rigid registration methods that are capable of mimicking human decisions. In the future, the deduction of knowledge-based corrections for different cohorts can be established automating such supervised learning approaches. BioMed Central 2017-06-21 /pmc/articles/PMC5480194/ /pubmed/28637483 http://dx.doi.org/10.1186/s13014-017-0842-8 Text en © The Author(s). 2017 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Stoiber, Eva Maria Bougatf, Nina Teske, Hendrik Bierstedt, Christian Oetzel, Dieter Debus, Jürgen Bendl, Rolf Giske, Kristina Analyzing human decisions in IGRT of head-and-neck cancer patients to teach image registration algorithms what experts know |
title | Analyzing human decisions in IGRT of head-and-neck cancer patients to teach image registration algorithms what experts know |
title_full | Analyzing human decisions in IGRT of head-and-neck cancer patients to teach image registration algorithms what experts know |
title_fullStr | Analyzing human decisions in IGRT of head-and-neck cancer patients to teach image registration algorithms what experts know |
title_full_unstemmed | Analyzing human decisions in IGRT of head-and-neck cancer patients to teach image registration algorithms what experts know |
title_short | Analyzing human decisions in IGRT of head-and-neck cancer patients to teach image registration algorithms what experts know |
title_sort | analyzing human decisions in igrt of head-and-neck cancer patients to teach image registration algorithms what experts know |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5480194/ https://www.ncbi.nlm.nih.gov/pubmed/28637483 http://dx.doi.org/10.1186/s13014-017-0842-8 |
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