File Download

There are no files associated with this item.

  Links for fulltext
     (May Require Subscription)
Supplementary

Conference Paper: Globally Guided Progressive Fusion Network for 3D Pancreas Segmentation

TitleGlobally Guided Progressive Fusion Network for 3D Pancreas Segmentation
Authors
KeywordsComputed tomography
End-to-end deep convolution network
Global guidance
Pancreas segmentation
Progressive fusion
Issue Date2019
PublisherSpringer
Citation
The 22nd International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2019), Shenzhen, China, 13-17 October 2019, Proceedings, pt. 2, p. 210-218 How to Cite?
AbstractRecently 3D volumetric organ segmentation attracts much research interest in medical image analysis due to its significance in computer aided diagnosis. This paper aims to address the pancreas segmentation task in 3D computed tomography volumes. We propose a novel end-to-end network, Globally Guided Progressive Fusion Network, as an effective and efficient solution to volumetric segmentation, which involves both global features and complicated 3D geometric information. A progressive fusion network is devised to extract 3D information from a moderate number of neighboring slices and predict a probability map for the segmentation of each slice. An independent branch for excavating global features from downsampled slices is further integrated into the network. Extensive experimental results demonstrate that our method achieves state-of-the-art performance on two pancreas datasets.
DescriptionPoster Sessions - Session 3: Segmentation and Registration 1 - no. M-3-M-176
Persistent Identifierhttp://hdl.handle.net/10722/284141
ISBN
ISSN
2023 SCImago Journal Rankings: 0.606
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorFang, C-
dc.contributor.authorLi, G-
dc.contributor.authorPan, C-
dc.contributor.authorLi, Y-
dc.contributor.authorYu, Y-
dc.date.accessioned2020-07-20T05:56:25Z-
dc.date.available2020-07-20T05:56:25Z-
dc.date.issued2019-
dc.identifier.citationThe 22nd International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2019), Shenzhen, China, 13-17 October 2019, Proceedings, pt. 2, p. 210-218-
dc.identifier.isbn9783030322441-
dc.identifier.issn0302-9743-
dc.identifier.urihttp://hdl.handle.net/10722/284141-
dc.descriptionPoster Sessions - Session 3: Segmentation and Registration 1 - no. M-3-M-176-
dc.description.abstractRecently 3D volumetric organ segmentation attracts much research interest in medical image analysis due to its significance in computer aided diagnosis. This paper aims to address the pancreas segmentation task in 3D computed tomography volumes. We propose a novel end-to-end network, Globally Guided Progressive Fusion Network, as an effective and efficient solution to volumetric segmentation, which involves both global features and complicated 3D geometric information. A progressive fusion network is devised to extract 3D information from a moderate number of neighboring slices and predict a probability map for the segmentation of each slice. An independent branch for excavating global features from downsampled slices is further integrated into the network. Extensive experimental results demonstrate that our method achieves state-of-the-art performance on two pancreas datasets.-
dc.languageeng-
dc.publisherSpringer-
dc.relation.ispartofInternational Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI)-
dc.subjectComputed tomography-
dc.subjectEnd-to-end deep convolution network-
dc.subjectGlobal guidance-
dc.subjectPancreas segmentation-
dc.subjectProgressive fusion-
dc.titleGlobally Guided Progressive Fusion Network for 3D Pancreas Segmentation-
dc.typeConference_Paper-
dc.identifier.emailYu, Y: yzyu@cs.hku.hk-
dc.identifier.authorityYu, Y=rp01415-
dc.identifier.doi10.1007/978-3-030-32245-8_24-
dc.identifier.scopuseid_2-s2.0-85075686219-
dc.identifier.hkuros310939-
dc.identifier.volume2-
dc.identifier.spage210-
dc.identifier.epage218-
dc.identifier.eissn1611-3349-
dc.identifier.isiWOS:000548438900024-
dc.publisher.placeCham-
dc.identifier.issnl0302-9743-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats