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Towards automated spine mobility quantification: a locally rigid ct to x-ray registration framework

Research output: Chapter in Book/Report/Conference proceedingConference paper

David Drobny, Marta Ranzini, Amanda Isaac, Tom Vercauteren, Sébastien Ourselin, David Choi, Marc Modat

Original languageEnglish
Title of host publicationBiomedical Image Registration - 9th International Workshop, WBIR 2020, Proceedings
EditorsZiga Spiclin, Jamie McClelland, Jan Kybic, Orcun Goksel
Number of pages11
ISBN (Print)9783030501198
Publication statusPublished - 1 Jan 2020
Event9th International Workshop on Biomedical Image Registration, WBIR 2020 - Portoroz, Slovenia
Duration: 1 Dec 20202 Dec 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12120 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference9th International Workshop on Biomedical Image Registration, WBIR 2020

King's Authors


Different pathologies of the vertebral column, such as scoliosis, require quantification of the mobility of individual vertebrae or of curves of the spine for treatment planning. Without the necessary mobility, vertebrae can not be safely re-positioned and fused. The current clinical workflow consists of radiologists or surgeons estimating angular differences of neighbouring vertebrae from different x-ray images. This procedure is time consuming and prone to inaccuracy. The proposed method automates this quantification by deforming a CT image in a physiologically reasonable way and matching it to the x-ray images of interest. We propose a proof of concept evaluation on synthetic data. The automatic and quantitative analysis enables reproducible results independent of the investigator.

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