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dc.contributor.authorDong, Nanqing
dc.contributor.authorKampffmeyer, Michael C.
dc.contributor.authorLiang, Xiaodan
dc.contributor.authorWang, Zeya
dc.contributor.authorDai, Wei
dc.contributor.authorXing, Eric P.
dc.date.accessioned2019-10-21T19:32:30Z
dc.date.available2019-10-21T19:32:30Z
dc.date.issued2018-09-26
dc.description.abstractThe cardiothoracic ratio (CTR), a clinical metric of heart size in chest X-rays (CXRs), is a key indicator of cardiomegaly. Manual measurement of CTR is time-consuming and can be affected by human subjectivity, making it desirable to design computer-aided systems that assist clinicians in the diagnosis process. Automatic CTR estimation through chest organ segmentation, however, requires large amounts of pixel-level annotated data, which is often unavailable. To alleviate this problem, we propose an unsupervised domain adaptation framework based on adversarial networks. The framework learns domain invariant feature representations from openly available data sources to produce accurate chest organ segmentation for unlabeled datasets. Specifically, we propose a model that enforces our intuition that prediction masks should be domain independent. Hence, we introduce a discriminator that distinguishes segmentation predictions from ground truth masks. We evaluate our system’s prediction based on the assessment of radiologists and demonstrate the clinical practicability for the diagnosis of cardiomegaly. We finally illustrate on the JSRT dataset that the semi-supervised performance of our model is also very promising.en_US
dc.descriptionSource at <a href=https://doi.org/10.1007/978-3-030-00934-2_61>https://doi.org/10.1007/978-3-030-00934-2_61</a>.en_US
dc.identifier.citationDong N., Kampffmeyer M., Liang X., Wang Z., Dai W. & Xing E. (2018) Unsupervised Domain Adaptation for Automatic Estimation of Cardiothoracic Ratio. In: Frangi A., Schnabel J., Davatzikos C., Alberola-López C., Fichtinger G. (eds) <i>Medical Image Computing and Computer Assisted Intervention – MICCAI 2018. MICCAI 2018. Lecture Notes in Computer Science, vol 11071</i>. Cham: Springer. https://doi.org/10.1007/978-3-030-00934-2_61en_US
dc.identifier.cristinIDFRIDAID 1631709
dc.identifier.doi10.1007/978-3-030-00934-2_61
dc.identifier.issn0302-9743
dc.identifier.issn1611-3349
dc.identifier.urihttps://hdl.handle.net/10037/16444
dc.language.isoengen_US
dc.publisherSpringeren_US
dc.relation.journalLecture Notes in Computer Science
dc.rights.accessRightsopenAccessen_US
dc.subjectVDP::Technology: 500::Medical technology: 620en_US
dc.subjectVDP::Teknologi: 500::Medisinsk teknologi: 620en_US
dc.subjectVDP::Medical disciplines: 700::Clinical medical disciplines: 750::Cardiology: 771en_US
dc.subjectVDP::Medisinske Fag: 700::Klinisk medisinske fag: 750::Kardiologi: 771en_US
dc.titleUnsupervised domain adaptation for automatic estimation of cardiothoracic ratioen_US
dc.typeJournal articleen_US
dc.typeTidsskriftartikkelen_US
dc.typePeer revieweden_US


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