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Late fusion of deep learning and hand-crafted features for Achilles tendon healing monitoring

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dc.abstract.enHealing process assessment of the Achilles tendon is usually a complex procedure that relies on a combination of biomechanical and medical imaging tests. As a result, diagnostics remains a tedious and long-lasting task. Recently, a novel method for the automatic assessment of tendon healing based on Magnetic Resonance Imaging and deep learning was introduced. The method assesses six parameters related to the treatment progress utilizing a modified pre-trained network, PCA-reduced space, and linear regression. In this paper, we propose to improve this approach by incorporating hand-crafted features. We first perform a feature selection in order to obtain optimal sets of mixed hand-crafted and deep learning predictors. With the use of approx. 20,000 MRI slices, we then train a meta-regression algorithm that performs the tendon healing assessment. Finally, we evaluate the method against scores given by an experienced radiologist. In comparison with the previous baseline method, our approach significantly improves correlation in all of the six parameters assessed. Furthermore, our method uses only one MRI protocol and saves up to 60\% of the time needed for data acquisition.
dc.affiliationUniwersytet Warszawski
dc.conference.countryChiny
dc.conference.datefinish2019-10-17
dc.conference.datestart2019-10-13
dc.conference.placeShenzhen
dc.conference.seriesMedical Image Computing and Computer-Assisted Intervention
dc.conference.seriesMedical Image Computing and Computer-Assisted Intervention
dc.conference.seriesshortcutMICCAI
dc.conference.shortcutMICCAI
dc.conference.weblinkhttps://www.miccai2019.org/
dc.contributor.authorKapiński, Norbert
dc.contributor.authorNowosielski, Jędrzej
dc.contributor.authorNowiński, Krzysztof
dc.contributor.authorMarchwiany, Maciej
dc.contributor.authorZieliński, Jakub
dc.contributor.authorCiszkowska-Łysoń, Beata
dc.contributor.authorBorucki, Bartosz
dc.contributor.authorTrzciński, Tomasz
dc.date.accessioned2024-01-25T05:00:02Z
dc.date.available2024-01-25T05:00:02Z
dc.date.copyright2019-09-11
dc.date.issued2019
dc.description.accesstimeBEFORE_PUBLICATION
dc.description.financeNie dotyczy
dc.description.versionFINAL_AUTHOR
dc.identifier.urihttps://repozytorium.uw.edu.pl//handle/item/110908
dc.identifier.weblinkhttps://arxiv.org/abs/1909.05687
dc.languageeng
dc.pbn.affiliationcomputer and information sciences
dc.rightsOther
dc.sciencecloudnosend
dc.titleLate fusion of deep learning and hand-crafted features for Achilles tendon healing monitoring
dc.typeJournalArticle
dspace.entity.typePublication