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Conference Paper: 3D deeply supervised network for automatic liver segmentation from CT volumes

Title3D deeply supervised network for automatic liver segmentation from CT volumes
Authors
Issue Date2016
PublisherSpringer.
Citation
19th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI 2016), Athens, Greece, 17-21 October 2016. In Ourselin, S, Joskowicz, L, Sabuncu, MR, et al. (Eds), Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016: 19th International Conference, Athens, Greece, October 17-21, 2016, Proceedings, Part II, p. 149-157. Cham, Switzerland: Springer, 2016 How to Cite?
AbstractAutomatic liver segmentation from CT volumes is a crucial prerequisite yet challenging task for computer-aided hepatic disease diagnosis and treatment. In this paper,we present a novel 3D deeply supervised network (3D DSN) to address this challenging task. The proposed 3D DSN takes advantage of a fully convolutional architecture which performs efficient end-to-end learning and inference. More importantly,we introduce a deep supervision mechanism during the learning process to combat potential optimization difficulties,and thus the model can acquire a much faster convergence rate and more powerful discrimination capability. On top of the high-quality score map produced by the 3D DSN,a conditional random field model is further employed to obtain refined segmentation results. We evaluated our framework on the public MICCAI-SLiver07 dataset. Extensive experiments demonstrated that our method achieves competitive segmentation results to state-of-the-art approaches with a much faster processing speed.
Persistent Identifierhttp://hdl.handle.net/10722/299537
ISBN
ISSN
2023 SCImago Journal Rankings: 0.606
Series/Report no.Lecture Notes in Computer Science ; 9901

 

DC FieldValueLanguage
dc.contributor.authorDou, Qi-
dc.contributor.authorChen, Hao-
dc.contributor.authorJin, Yueming-
dc.contributor.authorYu, Lequan-
dc.contributor.authorQin, Jing-
dc.contributor.authorHeng, Pheng Ann-
dc.date.accessioned2021-05-21T03:34:37Z-
dc.date.available2021-05-21T03:34:37Z-
dc.date.issued2016-
dc.identifier.citation19th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI 2016), Athens, Greece, 17-21 October 2016. In Ourselin, S, Joskowicz, L, Sabuncu, MR, et al. (Eds), Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016: 19th International Conference, Athens, Greece, October 17-21, 2016, Proceedings, Part II, p. 149-157. Cham, Switzerland: Springer, 2016-
dc.identifier.isbn9783319467221-
dc.identifier.issn0302-9743-
dc.identifier.urihttp://hdl.handle.net/10722/299537-
dc.description.abstractAutomatic liver segmentation from CT volumes is a crucial prerequisite yet challenging task for computer-aided hepatic disease diagnosis and treatment. In this paper,we present a novel 3D deeply supervised network (3D DSN) to address this challenging task. The proposed 3D DSN takes advantage of a fully convolutional architecture which performs efficient end-to-end learning and inference. More importantly,we introduce a deep supervision mechanism during the learning process to combat potential optimization difficulties,and thus the model can acquire a much faster convergence rate and more powerful discrimination capability. On top of the high-quality score map produced by the 3D DSN,a conditional random field model is further employed to obtain refined segmentation results. We evaluated our framework on the public MICCAI-SLiver07 dataset. Extensive experiments demonstrated that our method achieves competitive segmentation results to state-of-the-art approaches with a much faster processing speed.-
dc.languageeng-
dc.publisherSpringer.-
dc.relation.ispartofMedical Image Computing and Computer-Assisted Intervention – MICCAI 2016: 19th International Conference, Athens, Greece, October 17-21, 2016, Proceedings, Part II-
dc.relation.ispartofseriesLecture Notes in Computer Science ; 9901-
dc.title3D deeply supervised network for automatic liver segmentation from CT volumes-
dc.typeConference_Paper-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1007/978-3-319-46723-8_18-
dc.identifier.scopuseid_2-s2.0-84996523875-
dc.identifier.spage149-
dc.identifier.epage157-
dc.identifier.eissn1611-3349-
dc.publisher.placeCham, Switzerland-

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