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Joint multimodal segmentation of clinical CT and MR from hip arthroplasty patients

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

Marta Bianca Maria Ranzini, Michael Ebner, M. Jorge Cardoso, Anastasia Fotiadou, Tom Vercauteren, Johann Henckel, Alister Hart, Sébastien Ourselin, Marc Modat

Original languageEnglish
Title of host publicationComputational Methods and Clinical Applications in Musculoskeletal Imaging - 5th International Workshop, MSKI 2017, Held in Conjunction with MICCAI 2017, Revised Selected Papers
PublisherSpringer Verlag
Pages72-84
Number of pages13
Volume10734 LNCS
ISBN (Print)9783319741123
DOIs
Publication statusE-pub ahead of print - 10 Jan 2018
Event5th Workshop on Computational Methods and Clinical Applications in Musculoskeletal Imaging, MSKI 2017 Held in Conjunction with 20th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2017 - Quebec City, Canada
Duration: 10 Sep 201710 Sep 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10734 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference5th Workshop on Computational Methods and Clinical Applications in Musculoskeletal Imaging, MSKI 2017 Held in Conjunction with 20th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2017
CountryCanada
CityQuebec City
Period10/09/201710/09/2017

King's Authors

Abstract

Magnetic resonance imaging (MRI) is routinely employed to assess muscular response and presence of inflammatory reactions in patients treated with metal-on-metal hip arthroplasty, driving the decision for revision surgery. However, MRI is lacking contrast for bony structures and as a result orthopaedic surgical planning is mostly performed on computed tomography images. In this paper, we combine the complementary information of both modalities into a novel framework for the joint segmentation of healthy and pathological musculoskeletal structures as well as implants on all images. Our processing pipeline is fully automated and was designed to handle the highly anisotropic resolution of clinical MR images by means of super resolution reconstruction. The accuracy of the intra-subject multimodal registration was improved by employing a non-linear registration algorithm with hard constraints on the deformation of bony structures, while a multi-atlas segmentation propagation approach provided robustness to the large shape variability in the population. The suggested framework was evaluated in a leave-one-out cross-validation study on 20 hip sides. The proposed pipeline has potential for the extraction of clinically relevant imaging biomarkers for implant failure detection.

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