Semi-Automatic Lymph Node Segmentation in LN-MRI

dc.contributor.author Unal, Gozde
dc.contributor.author Slabaugh, Gregory G.
dc.contributor.author Ess, Andreas
dc.contributor.author Yezzi, Anthony
dc.contributor.author Tong, Fang
dc.contributor.author Tyan, Jason
dc.contributor.author Requardt, Martin
dc.contributor.author Krieg, Robert
dc.contributor.author Seethamraju, Ravi T.
dc.contributor.author Harisinghani, Mukesh
dc.contributor.author Weissleder, Ralph
dc.contributor.corporatename Georgia Institute of Technology. School of Electrical and Computer Engineering en_US
dc.contributor.corporatename Eidgenössische Technische Hochschule Zürich en_US
dc.contributor.corporatename Massachusetts General Hospital. Center For Molecular Imaging Research en_US
dc.contributor.corporatename Siemens Aktiengesellschaft. Medical Solutions en_US
dc.contributor.corporatename Siemens Corporate Research en_US
dc.date.accessioned 2013-09-18T13:41:38Z
dc.date.available 2013-09-18T13:41:38Z
dc.date.issued 2006-10
dc.description © 2006 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. en_US
dc.description Presented at the 2006 IEEE International Conference on Image Processing (ICIP 2006), 8-11 October 2006, Atlanta, GA.
dc.description DOI: 10.1109/ICIP.2006.312366
dc.description.abstract Accurate staging of nodal cancer still relies on surgical exploration because many primary malignancies spread via lymphatic dissemination. The purpose of this study was to utilize nanoparticle-enhanced lymphotropic magnetic resonance imaging (LN-MRI) to explore semi-automated noninvasive nodal cancer staging. We present a joint image segmentation and registration approach, which makes use of the problem specific information to increase the robustness of the algorithm to noise and weak contrast often observed in medical imaging applications. The effectiveness of the approach is demonstrated with a given lymph node segmentation problem in post-contrast pelvic MRI sequences en_US
dc.embargo.terms null en_US
dc.identifier.citation Unal, G.; Slabaugh, G.; Ess, A.; Yezzi, A.; Fang, T.; Tyan, J.; Requardt, M.; Krieg, R.; Seethamraju, R.; Harisinghani, M.; & Weissleder, R. (2006). "Semi-Automatic Lymph Node Segmentation in LN-MRI”. Proceedings of the 2006 IEEE International Conference on Image Processing (ICIP 2006), (October 2006), pp.77-80. en_US
dc.identifier.doi 10.1109/ICIP.2006.312366
dc.identifier.isbn 1-4244-0480-0
dc.identifier.issn 1522-4880
dc.identifier.uri http://hdl.handle.net/1853/48965
dc.language.iso en_US en_US
dc.publisher Georgia Institute of Technology en_US
dc.publisher.original Institute of Electrical and Electronics Engineers
dc.subject Biomedical image processing en_US
dc.subject Biomedical magnetic resonance imaging en_US
dc.subject Image segmentation en_US
dc.subject Medical diagnosis en_US
dc.title Semi-Automatic Lymph Node Segmentation in LN-MRI en_US
dc.type Text
dc.type.genre Proceedings
dspace.entity.type Publication
local.contributor.author Yezzi, Anthony
local.contributor.corporatename School of Electrical and Computer Engineering
local.contributor.corporatename College of Engineering
relation.isAuthorOfPublication 53ee63a2-04fd-454f-b094-02a4601962d8
relation.isOrgUnitOfPublication 5b7adef2-447c-4270-b9fc-846bd76f80f2
relation.isOrgUnitOfPublication 7c022d60-21d5-497c-b552-95e489a06569
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