MuSIC: Multi-Sequential Interactive Co-Registration for Cancer Imaging Data based on Segmentation Masks

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dc.contributor.authorEichner, Tanjaen_US
dc.contributor.authorMörth, Ericen_US
dc.contributor.authorWagner-Larsen, Kari S.en_US
dc.contributor.authorLura, Njålen_US
dc.contributor.authorHaldorsen, Ingfrid S.en_US
dc.contributor.authorGröller, Eduarden_US
dc.contributor.authorBruckner, Stefanen_US
dc.contributor.authorSmit, Noeska N.en_US
dc.contributor.editorRenata G. Raidouen_US
dc.contributor.editorBjörn Sommeren_US
dc.contributor.editorTorsten W. Kuhlenen_US
dc.contributor.editorMichael Kroneen_US
dc.contributor.editorThomas Schultzen_US
dc.contributor.editorHsiang-Yun Wuen_US
dc.date.accessioned2022-09-19T11:46:31Z
dc.date.available2022-09-19T11:46:31Z
dc.date.issued2022
dc.description.abstractIn gynecologic cancer imaging, multiple magnetic resonance imaging (MRI) sequences are acquired per patient to reveal different tissue characteristics. However, after image acquisition, the anatomical structures can be misaligned in the various sequences due to changing patient location in the scanner and organ movements. The co-registration process aims to align the sequences to allow for multi-sequential tumor imaging analysis. However, automatic co-registration often leads to unsatisfying results. To address this problem, we propose the web-based application MuSIC (Multi-Sequential Interactive Co-registration). The approach allows medical experts to co-register multiple sequences simultaneously based on a pre-defined segmentation mask generated for one of the sequences. Our contributions lie in our proposed workflow. First, a shape matching algorithm based on dual annealing searches for the tumor position in each sequence. The user can then interactively adapt the proposed segmentation positions if needed. During this procedure, we include a multi-modal magic lens visualization for visual quality assessment. Then, we register the volumes based on the segmentation mask positions. We allow for both rigid and deformable registration. Finally, we conducted a usability analysis with seven medical and machine learning experts to verify the utility of our approach. Our participants highly appreciate the multi-sequential setup and see themselves using MuSIC in the future.en_US
dc.description.sectionheadersSegmentation, Registration, and Networks
dc.description.seriesinformationEurographics Workshop on Visual Computing for Biology and Medicine
dc.identifier.doi10.2312/vcbm.20221190
dc.identifier.isbn978-3-03868-177-9
dc.identifier.issn2070-5786
dc.identifier.pages81-91
dc.identifier.pages11 pages
dc.identifier.urihttps://doi.org/10.2312/vcbm.20221190
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/vcbm20221190
dc.publisherThe Eurographics Associationen_US
dc.rightsAttribution 4.0 International License
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectCCS Concepts: Applied computing --> Health informatics; Human-centered computing --> Visualization design and evaluation methods"
dc.subjectApplied computing
dc.subjectHealth informatics
dc.subjectHuman centered computing
dc.subjectVisualization design and evaluation methods"
dc.titleMuSIC: Multi-Sequential Interactive Co-Registration for Cancer Imaging Data based on Segmentation Masksen_US
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