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dc.contributor.authorChen, Zhonghua
dc.contributor.authorRistaniemi, Tapani
dc.contributor.authorCong, Fengyu
dc.contributor.authorWang, Hongkai
dc.contributor.editorHan, Min
dc.contributor.editorQin, Sitian
dc.contributor.editorZhang, Nian
dc.date.accessioned2024-02-26T10:44:13Z
dc.date.available2024-02-26T10:44:13Z
dc.date.issued2020
dc.identifier.citationChen, Z., Ristaniemi, T., Cong, F., & Wang, H. (2020). Multi-resolution Statistical Shape Models for Multi-organ Shape Modelling. In M. Han, S. Qin, & N. Zhang (Eds.), <i>ISNN 2020 : Advances in Neural Networks : 17th International Symposium on Neural Networks, Proceedings</i> (pp. 74-84). Springer. Lecture Notes in Computer Science, 12557. <a href="https://doi.org/10.1007/978-3-030-64221-1_7" target="_blank">https://doi.org/10.1007/978-3-030-64221-1_7</a>
dc.identifier.otherCONVID_47215546
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/93664
dc.description.abstractStatistical shape models (SSMs) are widely used in medical image segmentation. However, traditional SSM methods suffer from the High-Dimension-Low-Sample-Size (HDLSS) problem in modelling. In this work, we extend the state-of-the-art multi-resolution SSM approach from two dimension (2D) to three dimension (3D) and from single organ to multiple organs. Then we proposed a multi-resolution multi-organ 3D SSM method that uses a downsampling-and-interpolation strategy to overcome HDLSS problem. We also use an inter-surface-point distance thresholding scheme to achieve multi-resolution modelling effect. Our method is tested on the modelling of multiple mouse abdominal organs from mouse micro-CT images in three different resolution levels, including global level, single organ level and local organ level. The minimum specificity error and generalization error of this method are less than 0.3 mm, which are close to the pixel resolution of mouse micro-CT images (0.2 mm) and better than the modelling results of traditional principal component analysis (PCA) method.en
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherSpringer
dc.relation.ispartofISNN 2020 : Advances in Neural Networks : 17th International Symposium on Neural Networks, Proceedings
dc.relation.ispartofseriesLecture Notes in Computer Science
dc.rightsIn Copyright
dc.subject.othermulti-resolution multi-organ SSM
dc.subject.otherPCA
dc.subject.otherHDLSS
dc.subject.othermouse micro-CT image
dc.titleMulti-resolution Statistical Shape Models for Multi-organ Shape Modelling
dc.typeconferenceObject
dc.identifier.urnURN:NBN:fi:jyu-202402262129
dc.contributor.laitosInformaatioteknologian tiedekuntafi
dc.contributor.laitosFaculty of Information Technologyen
dc.contributor.oppiaineTietotekniikkafi
dc.contributor.oppiaineMathematical Information Technologyen
dc.type.urihttp://purl.org/eprint/type/ConferencePaper
dc.relation.isbn978-3-030-64220-4
dc.type.coarhttp://purl.org/coar/resource_type/c_5794
dc.description.reviewstatuspeerReviewed
dc.format.pagerange74-84
dc.relation.issn0302-9743
dc.type.versionacceptedVersion
dc.rights.copyright© 2020 Springer Nature Switzerland AG
dc.rights.accesslevelopenAccessfi
dc.relation.conferenceInternational Symposium on Neural Networks
dc.subject.ysotilastolliset mallit
dc.subject.yso3D-mallinnus
dc.subject.ysotietokonetomografia
dc.subject.ysosisäelimet
dc.format.contentfulltext
jyx.subject.urihttp://www.yso.fi/onto/yso/p26278
jyx.subject.urihttp://www.yso.fi/onto/yso/p26739
jyx.subject.urihttp://www.yso.fi/onto/yso/p20535
jyx.subject.urihttp://www.yso.fi/onto/yso/p12475
dc.rights.urlhttp://rightsstatements.org/page/InC/1.0/?language=en
dc.relation.doi10.1007/978-3-030-64221-1_7
jyx.fundinginformationThis study was funded by the general program of the National Natural Science Fund of China (No. 81971693, 81401475), the Science and Technology Innovation Fund of Dalian City (2018J12GX042) and the Fundamental Research Funds for the Central Universities (DUT19JC01). We thank the Molecular Imaging Centre of the University of California, Los Angeles for providing 98 mouse CT images and the scholarships from China Scholarship Council (No. 201806060163).
dc.type.okmA4


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