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dc.contributor.author Won, Dongkyu ko
dc.contributor.author An, Sion ko
dc.contributor.author Park, Sang Hyun ko
dc.contributor.author Ye, Dong Hye ko
dc.date.accessioned 2021-01-29T07:25:03Z -
dc.date.available 2021-01-29T07:25:03Z -
dc.date.created 2020-10-29 -
dc.date.issued 2020-10-05 -
dc.identifier.citation International Conference on Medical Image Computing and Computer Assisted Intervention, pp.68 - 78 -
dc.identifier.isbn 9783030593537 -
dc.identifier.issn 0302-9743 -
dc.identifier.uri http://hdl.handle.net/20.500.11750/12885 -
dc.description.abstract Low-dose CT denoising has been studied to reduce radiation exposure to patients. Recently, deep learning-based techniques have improved the CT denoising performance, but it is difficult to reflect the characteristics of signals concerning different frequencies properly. Even though high-frequency components play an essential role in denoising, the deep network with a large number of parameters doesn’t concern it and tends to generate the image still having noise and losing the structure. To address this problem, we propose a novel CT denoising method that decomposes high- and low-frequency features and learns more parameters on important features during training. We introduce a network consisting of Octave convolution layers that take feature maps with two frequencies and extract information directly from both maps with inter- and intra-convolutions. The proposed method effectively reduces the noise while maintaining edge sharpness by reducing the spatial redundancy in the network. For evaluation, the 2016 AAPM Low-Dose CT challenge data set was used. The proposed method achieved better performance than the existing CT denoising methods in quantitative and qualitative evaluations. © 2020, Springer Nature Switzerland AG. -
dc.language English -
dc.publisher Springer Science and Business Media Deutschland GmbH -
dc.title Low-Dose CT Denoising Using Octave Convolution with High and Low Frequency Bands -
dc.type Conference -
dc.identifier.doi 10.1007/978-3-030-59354-4_7 -
dc.identifier.scopusid 2-s2.0-85092928213 -
dc.type.local Article(Overseas) -
dc.type.rims CONF -
dc.description.journalClass 1 -
dc.contributor.localauthor Park, Sang Hyun -
dc.contributor.nonIdAuthor Ye, Dong Hye -
dc.identifier.citationStartPage 68 -
dc.identifier.citationEndPage 78 -
dc.identifier.citationTitle International Conference on Medical Image Computing and Computer Assisted Intervention -
dc.identifier.conferencecountry PE -
dc.identifier.conferencelocation Lima -


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