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Conference Paper: Liver Guided Pancreas Segmentation

TitleLiver Guided Pancreas Segmentation
Authors
KeywordsPancreas segmentation
location prior
convolutional neural network
CT
coarse-to-fine
Issue Date2020
PublisherIEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000080
Citation
Proceedings of the 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), Iowa City, IA, USA, 3-7 April 2020, p. 1201-1204 How to Cite?
AbstractIn this paper, we propose and validate a location prior guided automatic pancreas segmentation framework based on 3D convolutional neural network (CNN). To guide pancreas segmentation, centroid of the pancreas used to determine its bounding box is calculated using the location of the liver which is firstly segmented by a 2D CNN. A linear relationship between centroids of the pancreas and the liver is proposed. After that, a 3D CNN is employed the input of which is the bounding box of the pancreas to get the final segmentation. A publicly accessible pancreas dataset including 54 subjects is used to quantify the performance of the proposed framework. Experimental results reveal outstanding performance of the proposed method in terms of both computational efficiency and segmentation accuracy compared to non location guided segmentation. To be specific, the running time is 15 times faster and the segmentation accuracy in terms of Dice is higher by 4.29% (76.42% versus 80.71%).
Persistent Identifierhttp://hdl.handle.net/10722/304348
ISSN
2020 SCImago Journal Rankings: 0.601
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorZhang, Y-
dc.contributor.authorWu, J-
dc.contributor.authorWang, S-
dc.contributor.authorLiu, AY-
dc.contributor.authorChen, Y-
dc.contributor.authorWu, EX-
dc.contributor.authorTang, X-
dc.date.accessioned2021-09-23T08:58:47Z-
dc.date.available2021-09-23T08:58:47Z-
dc.date.issued2020-
dc.identifier.citationProceedings of the 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), Iowa City, IA, USA, 3-7 April 2020, p. 1201-1204-
dc.identifier.issn1945-7928-
dc.identifier.urihttp://hdl.handle.net/10722/304348-
dc.description.abstractIn this paper, we propose and validate a location prior guided automatic pancreas segmentation framework based on 3D convolutional neural network (CNN). To guide pancreas segmentation, centroid of the pancreas used to determine its bounding box is calculated using the location of the liver which is firstly segmented by a 2D CNN. A linear relationship between centroids of the pancreas and the liver is proposed. After that, a 3D CNN is employed the input of which is the bounding box of the pancreas to get the final segmentation. A publicly accessible pancreas dataset including 54 subjects is used to quantify the performance of the proposed framework. Experimental results reveal outstanding performance of the proposed method in terms of both computational efficiency and segmentation accuracy compared to non location guided segmentation. To be specific, the running time is 15 times faster and the segmentation accuracy in terms of Dice is higher by 4.29% (76.42% versus 80.71%).-
dc.languageeng-
dc.publisherIEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000080-
dc.relation.ispartofIEEE International Symposium on Biomedical Imaging Proceedings-
dc.rightsIEEE International Symposium on Biomedical Imaging Proceedings. Copyright © IEEE.-
dc.rights©2020 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.-
dc.subjectPancreas segmentation-
dc.subjectlocation prior-
dc.subjectconvolutional neural network-
dc.subjectCT-
dc.subjectcoarse-to-fine-
dc.titleLiver Guided Pancreas Segmentation-
dc.typeConference_Paper-
dc.identifier.emailWu, EX: ewu@eee.hku.hk-
dc.identifier.authorityWu, EX=rp00193-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1109/ISBI45749.2020.9098388-
dc.identifier.scopuseid_2-s2.0-85085855651-
dc.identifier.hkuros325448-
dc.identifier.spage1201-
dc.identifier.epage1204-
dc.identifier.isiWOS:000578080300246-
dc.publisher.placeUnited States-

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