title: Analyzing human decisions in IGRT of head-and-neck cancer patients to teach image registration algorithms what experts know
creator: Stoiber, Eva Maria
creator: Bougatf, Nina
creator: Teske, Hendrik
creator: Bierstedt, Christian
creator: Oetzel, Dieter
creator: Debus, Jürgen
creator: Bendl, Rolf
creator: Giske, Kristina
subject: ddc-610
subject: 610 Medical sciences Medicine
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.
publisher: BioMed Central
date: 2017
type: Article
type: info:eu-repo/semantics/article
type: NonPeerReviewed
format: application/pdf
identifier: https://archiv.ub.uni-heidelberg.de/volltextserverhttps://archiv.ub.uni-heidelberg.de/volltextserver/23125/1/13014_2017_Article_842.pdf
identifier: DOI:
identifier: urn:nbn:de:bsz:16-heidok-231253
identifier:   Stoiber, Eva Maria ; Bougatf, Nina ; Teske, Hendrik ; Bierstedt, Christian ; Oetzel, Dieter ; Debus, Jürgen ; Bendl, Rolf ; Giske, Kristina  (2017) Analyzing human decisions in IGRT of head-and-neck cancer patients to teach image registration algorithms what experts know.  Radiation Oncology, 12 (104).  pp. 1-7.  ISSN 1748-717X     
relation: https://archiv.ub.uni-heidelberg.de/volltextserver/23125/
rights: info:eu-repo/semantics/openAccess
rights: Please see front page of the work (Sorry, Dublin Core plugin does not recognise license id)
language: eng