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Conference Paper: Towards automatic semantic segmentation in volumetric ultrasound

TitleTowards automatic semantic segmentation in volumetric ultrasound
Authors
Issue Date2017
PublisherSpringer.
Citation
20th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI 2017), Quebec City, Canada, 11-13 September 2017. In Descoteaux, M, Maier-Hein, L, Franz, A, et al. (Eds.), Medical Image Computing and Computer Assisted Intervention − MICCAI 2017: 20th International Conference, Quebec City, QC, Canada, September 11-13, 2017, Proceedings, Part I, p. 711-719. Cham, Switzerland: Springer, 2017 How to Cite?
Abstract3D ultrasound is rapidly emerging as a viable imaging modality for routine prenatal examinations. However, lacking of efficient tools to decompose the volumetric data greatly limits its widespread. In this paper, we are looking at the problem of volumetric segmentation in ultrasound to promote the volume-based, precise maternal and fetal health monitoring. Our contribution is threefold. First, we propose the first and fully automatic framework for the simultaneous segmentation of multiple objects, including fetus, gestational sac and placenta, in ultrasound volumes, which remains as a rarely-studied but great challenge. Second, based on our customized 3D Fully Convolutional Network, we propose to inject a Recurrent Neural Network (RNN) to flexibly explore 3D semantic knowledge from a novel, sequential perspective, and therefore significantly refine the local segmentation result which is initially corrupted by the ubiquitous boundary uncertainty in ultrasound volumes. Third, considering sequence hierarchy, we introduce a hierarchical deep supervision mechanism to effectively boost the information flow within RNN and further improve the semantic segmentation results. Extensively validated on our in-house large datasets, our approach achieves superior performance and presents to be promising in boosting the interpretation of prenatal ultrasound volumes. Our framework is general and can be easily extended to other volumetric ultrasound segmentation tasks.
Persistent Identifierhttp://hdl.handle.net/10722/299556
ISBN
ISSN
2023 SCImago Journal Rankings: 0.606
Series/Report no.Lecture Notes in Computer Science ; 10433

 

DC FieldValueLanguage
dc.contributor.authorYang, Xin-
dc.contributor.authorYu, Lequan-
dc.contributor.authorLi, Shengli-
dc.contributor.authorWang, Xu-
dc.contributor.authorWang, Na-
dc.contributor.authorQin, Jing-
dc.contributor.authorNi, Dong-
dc.contributor.authorHeng, Pheng Ann-
dc.date.accessioned2021-05-21T03:34:39Z-
dc.date.available2021-05-21T03:34:39Z-
dc.date.issued2017-
dc.identifier.citation20th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI 2017), Quebec City, Canada, 11-13 September 2017. In Descoteaux, M, Maier-Hein, L, Franz, A, et al. (Eds.), Medical Image Computing and Computer Assisted Intervention − MICCAI 2017: 20th International Conference, Quebec City, QC, Canada, September 11-13, 2017, Proceedings, Part I, p. 711-719. Cham, Switzerland: Springer, 2017-
dc.identifier.isbn9783319661810-
dc.identifier.issn0302-9743-
dc.identifier.urihttp://hdl.handle.net/10722/299556-
dc.description.abstract3D ultrasound is rapidly emerging as a viable imaging modality for routine prenatal examinations. However, lacking of efficient tools to decompose the volumetric data greatly limits its widespread. In this paper, we are looking at the problem of volumetric segmentation in ultrasound to promote the volume-based, precise maternal and fetal health monitoring. Our contribution is threefold. First, we propose the first and fully automatic framework for the simultaneous segmentation of multiple objects, including fetus, gestational sac and placenta, in ultrasound volumes, which remains as a rarely-studied but great challenge. Second, based on our customized 3D Fully Convolutional Network, we propose to inject a Recurrent Neural Network (RNN) to flexibly explore 3D semantic knowledge from a novel, sequential perspective, and therefore significantly refine the local segmentation result which is initially corrupted by the ubiquitous boundary uncertainty in ultrasound volumes. Third, considering sequence hierarchy, we introduce a hierarchical deep supervision mechanism to effectively boost the information flow within RNN and further improve the semantic segmentation results. Extensively validated on our in-house large datasets, our approach achieves superior performance and presents to be promising in boosting the interpretation of prenatal ultrasound volumes. Our framework is general and can be easily extended to other volumetric ultrasound segmentation tasks.-
dc.languageeng-
dc.publisherSpringer.-
dc.relation.ispartofMedical Image Computing and Computer Assisted Intervention − MICCAI 2017: 20th International Conference, Quebec City, QC, Canada, September 11-13, 2017, Proceedings, Part I-
dc.relation.ispartofseriesLecture Notes in Computer Science ; 10433-
dc.titleTowards automatic semantic segmentation in volumetric ultrasound-
dc.typeConference_Paper-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1007/978-3-319-66182-7_81-
dc.identifier.scopuseid_2-s2.0-85029360871-
dc.identifier.spage711-
dc.identifier.epage719-
dc.identifier.eissn1611-3349-
dc.publisher.placeCham, Switzerland-

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