eprintid: 23125
rev_number: 13
eprint_status: archive
userid: 1589
dir: disk0/00/02/31/25
datestamp: 2017-06-28 13:23:10
lastmod: 2024-01-17 09:05:31
status_changed: 2017-06-28 13:23:10
type: article
metadata_visibility: show
creators_name: Stoiber, Eva Maria
creators_name: Bougatf, Nina
creators_name: Teske, Hendrik
creators_name: Bierstedt, Christian
creators_name: Oetzel, Dieter
creators_name: Debus, Jürgen
creators_name: Bendl, Rolf
creators_name: Giske, Kristina
title: Analyzing human decisions in IGRT of head-and-neck cancer patients to teach image registration algorithms what experts know
subjects: ddc-610
divisions: i-850300
divisions: i-911400
abstract: 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.
date: 2017
publisher: BioMed Central
id_scheme: DOI
ppn_swb: 1659389771
own_urn: urn:nbn:de:bsz:16-heidok-231253
language: eng
bibsort: STOIBEREVAANALYZINGH2017
full_text_status: public
publication: Radiation Oncology
volume: 12
number: 104
place_of_pub: London
pagerange: 1-7
issn: 1748-717X
citation:   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     
document_url: https://archiv.ub.uni-heidelberg.de/volltextserver/23125/1/13014_2017_Article_842.pdf