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Article: Towards automated semantic segmentation in prenatal volumetric ultrasound

TitleTowards automated semantic segmentation in prenatal volumetric ultrasound
Authors
KeywordsPrenatal examination
semantic segmentation
fully convolutional networks
recurrent neural networks
volumetric ultrasound
Issue Date2019
Citation
IEEE Transactions on Medical Imaging, 2019, v. 38, n. 1, p. 180-193 How to Cite?
AbstractVolumetric ultrasound is rapidly emerging as a viable imaging modality for routine prenatal examinations. Biometrics obtained from the volumetric segmentation shed light on the reformation of precise maternal and fetal health monitoring. However, the poor image quality, low contrast, boundary ambiguity, and complex anatomy shapes conspire toward a great lack of efficient tools for the segmentation. It makes 3-D ultrasound difficult to interpret and hinders the widespread of 3-D ultrasound in obstetrics. In this paper, we are looking at the problem of semantic segmentation in prenatal ultrasound volumes. Our contribution is threefold: 1) we propose the first and fully automatic framework to simultaneously segment multiple anatomical structures with intensive clinical interest, including fetus, gestational sac, and placenta, which remains a rarely studied and arduous challenge; 2) we propose a composite architecture for dense labeling, in which a customized 3-D fully convolutional network explores spatial intensity concurrency for initial labeling, while a multi-directional recurrent neural network (RNN) encodes spatial sequentiality to combat boundary ambiguity for significant refinement; and 3) we introduce a hierarchical deep supervision mechanism to boost the information flow within RNN and fit the latent sequence hierarchy in fine scales, and further improve the segmentation results. Extensively verified on in-house large data sets, our method illustrates a superior segmentation performance, decent agreements with expert measurements and high reproducibilities against scanning variations, and thus is promising in advancing the prenatal ultrasound examinations.
Persistent Identifierhttp://hdl.handle.net/10722/299575
ISSN
2023 Impact Factor: 8.9
2023 SCImago Journal Rankings: 3.703
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorYang, Xin-
dc.contributor.authorYu, Lequan-
dc.contributor.authorLi, Shengli-
dc.contributor.authorWen, Huaxuan-
dc.contributor.authorLuo, Dandan-
dc.contributor.authorBian, Cheng-
dc.contributor.authorQin, Jing-
dc.contributor.authorNi, Dong-
dc.contributor.authorHeng, Pheng Ann-
dc.date.accessioned2021-05-21T03:34:42Z-
dc.date.available2021-05-21T03:34:42Z-
dc.date.issued2019-
dc.identifier.citationIEEE Transactions on Medical Imaging, 2019, v. 38, n. 1, p. 180-193-
dc.identifier.issn0278-0062-
dc.identifier.urihttp://hdl.handle.net/10722/299575-
dc.description.abstractVolumetric ultrasound is rapidly emerging as a viable imaging modality for routine prenatal examinations. Biometrics obtained from the volumetric segmentation shed light on the reformation of precise maternal and fetal health monitoring. However, the poor image quality, low contrast, boundary ambiguity, and complex anatomy shapes conspire toward a great lack of efficient tools for the segmentation. It makes 3-D ultrasound difficult to interpret and hinders the widespread of 3-D ultrasound in obstetrics. In this paper, we are looking at the problem of semantic segmentation in prenatal ultrasound volumes. Our contribution is threefold: 1) we propose the first and fully automatic framework to simultaneously segment multiple anatomical structures with intensive clinical interest, including fetus, gestational sac, and placenta, which remains a rarely studied and arduous challenge; 2) we propose a composite architecture for dense labeling, in which a customized 3-D fully convolutional network explores spatial intensity concurrency for initial labeling, while a multi-directional recurrent neural network (RNN) encodes spatial sequentiality to combat boundary ambiguity for significant refinement; and 3) we introduce a hierarchical deep supervision mechanism to boost the information flow within RNN and fit the latent sequence hierarchy in fine scales, and further improve the segmentation results. Extensively verified on in-house large data sets, our method illustrates a superior segmentation performance, decent agreements with expert measurements and high reproducibilities against scanning variations, and thus is promising in advancing the prenatal ultrasound examinations.-
dc.languageeng-
dc.relation.ispartofIEEE Transactions on Medical Imaging-
dc.subjectPrenatal examination-
dc.subjectsemantic segmentation-
dc.subjectfully convolutional networks-
dc.subjectrecurrent neural networks-
dc.subjectvolumetric ultrasound-
dc.titleTowards automated semantic segmentation in prenatal volumetric ultrasound-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1109/TMI.2018.2858779-
dc.identifier.pmid30040635-
dc.identifier.scopuseid_2-s2.0-85050380584-
dc.identifier.volume38-
dc.identifier.issue1-
dc.identifier.spage180-
dc.identifier.epage193-
dc.identifier.eissn1558-254X-
dc.identifier.isiWOS:000455110500018-

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