dc.contributor.author | Chen, Zhonghua | |
dc.contributor.author | Ristaniemi, Tapani | |
dc.contributor.author | Cong, Fengyu | |
dc.contributor.author | Wang, Hongkai | |
dc.contributor.editor | Han, Min | |
dc.contributor.editor | Qin, Sitian | |
dc.contributor.editor | Zhang, Nian | |
dc.date.accessioned | 2024-02-26T10:44:13Z | |
dc.date.available | 2024-02-26T10:44:13Z | |
dc.date.issued | 2020 | |
dc.identifier.citation | Chen, 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.other | CONVID_47215546 | |
dc.identifier.uri | https://jyx.jyu.fi/handle/123456789/93664 | |
dc.description.abstract | Statistical 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.mimetype | application/pdf | |
dc.language.iso | eng | |
dc.publisher | Springer | |
dc.relation.ispartof | ISNN 2020 : Advances in Neural Networks : 17th International Symposium on Neural Networks, Proceedings | |
dc.relation.ispartofseries | Lecture Notes in Computer Science | |
dc.rights | In Copyright | |
dc.subject.other | multi-resolution multi-organ SSM | |
dc.subject.other | PCA | |
dc.subject.other | HDLSS | |
dc.subject.other | mouse micro-CT image | |
dc.title | Multi-resolution Statistical Shape Models for Multi-organ Shape Modelling | |
dc.type | conferenceObject | |
dc.identifier.urn | URN:NBN:fi:jyu-202402262129 | |
dc.contributor.laitos | Informaatioteknologian tiedekunta | fi |
dc.contributor.laitos | Faculty of Information Technology | en |
dc.contributor.oppiaine | Tietotekniikka | fi |
dc.contributor.oppiaine | Mathematical Information Technology | en |
dc.type.uri | http://purl.org/eprint/type/ConferencePaper | |
dc.relation.isbn | 978-3-030-64220-4 | |
dc.type.coar | http://purl.org/coar/resource_type/c_5794 | |
dc.description.reviewstatus | peerReviewed | |
dc.format.pagerange | 74-84 | |
dc.relation.issn | 0302-9743 | |
dc.type.version | acceptedVersion | |
dc.rights.copyright | © 2020 Springer Nature Switzerland AG | |
dc.rights.accesslevel | openAccess | fi |
dc.relation.conference | International Symposium on Neural Networks | |
dc.subject.yso | tilastolliset mallit | |
dc.subject.yso | 3D-mallinnus | |
dc.subject.yso | tietokonetomografia | |
dc.subject.yso | sisäelimet | |
dc.format.content | fulltext | |
jyx.subject.uri | http://www.yso.fi/onto/yso/p26278 | |
jyx.subject.uri | http://www.yso.fi/onto/yso/p26739 | |
jyx.subject.uri | http://www.yso.fi/onto/yso/p20535 | |
jyx.subject.uri | http://www.yso.fi/onto/yso/p12475 | |
dc.rights.url | http://rightsstatements.org/page/InC/1.0/?language=en | |
dc.relation.doi | 10.1007/978-3-030-64221-1_7 | |
jyx.fundinginformation | This 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.okm | A4 | |