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Conference Paper: 3D FractalNet: Dense volumetric segmentation for cardiovascular MRI volumes

Title3D FractalNet: Dense volumetric segmentation for cardiovascular MRI volumes
Authors
Issue Date2017
PublisherSpringer.
Citation
First International Workshops on Reconstruction and Analysis of Moving Body Organs (RAMBO 2016) and International Workshop on Whole-Heart and Great Vessel Segmentation from 3D Cardiovascular MRI in Congenital Heart Disease (HVSMR 2016), Held in Conjunction with MICCAI 2016, Athens, Greece, 17 October 2016. In Zuluaga, MA, Bhatia, K, Kainz, B, et al. (Eds.), Reconstruction, Segmentation, and Analysis of Medical Images: First International Workshops, RAMBO 2016 and HVSMR 2016, Held in Conjunction with MICCAI 2016, Athens, Greece, October 17, 2016, Revised Selected Papers, p. 103-110. Cham, Switzerland: Springer, 2017 How to Cite?
AbstractCardiac image segmentation plays a crucial role in various medical applications. However, differentiating branchy structures and slicing fuzzy boundaries from cardiovascular MRI volumes remain very challenging tasks. In this paper, we propose a novel deeply-supervised 3D fractal network for efficient automated whole heart and great vessel segmentation in MRI volumes. The proposed 3D fractal network takes advantage of fully convolutional architecture to perform efficient, precise and volume-to-volume prediction. Notably, by recursively applying a single expansion rule, we construct our network in a novel self-similar fractal scheme and thus promote it in combining hierarchical clues for accurate segmentation. More importantly, we employ deep supervision mechanism to alleviate the vanishing gradients problem and improve the training efficiency of our network on small medical image dataset. We evaluated our method on the HVSMR 2016 Challenge dataset. Extensive experimental results demonstrated the superior performance of our method, ranking top in both two phases.
Persistent Identifierhttp://hdl.handle.net/10722/299543
ISBN
ISSN
2023 SCImago Journal Rankings: 0.606
ISI Accession Number ID
Series/Report no.Lecture Notes in Computer Science ; 10129

 

DC FieldValueLanguage
dc.contributor.authorYu, Lequan-
dc.contributor.authorYang, Xin-
dc.contributor.authorQin, Jing-
dc.contributor.authorHeng, Pheng Ann-
dc.date.accessioned2021-05-21T03:34:38Z-
dc.date.available2021-05-21T03:34:38Z-
dc.date.issued2017-
dc.identifier.citationFirst International Workshops on Reconstruction and Analysis of Moving Body Organs (RAMBO 2016) and International Workshop on Whole-Heart and Great Vessel Segmentation from 3D Cardiovascular MRI in Congenital Heart Disease (HVSMR 2016), Held in Conjunction with MICCAI 2016, Athens, Greece, 17 October 2016. In Zuluaga, MA, Bhatia, K, Kainz, B, et al. (Eds.), Reconstruction, Segmentation, and Analysis of Medical Images: First International Workshops, RAMBO 2016 and HVSMR 2016, Held in Conjunction with MICCAI 2016, Athens, Greece, October 17, 2016, Revised Selected Papers, p. 103-110. Cham, Switzerland: Springer, 2017-
dc.identifier.isbn9783319522791-
dc.identifier.issn0302-9743-
dc.identifier.urihttp://hdl.handle.net/10722/299543-
dc.description.abstractCardiac image segmentation plays a crucial role in various medical applications. However, differentiating branchy structures and slicing fuzzy boundaries from cardiovascular MRI volumes remain very challenging tasks. In this paper, we propose a novel deeply-supervised 3D fractal network for efficient automated whole heart and great vessel segmentation in MRI volumes. The proposed 3D fractal network takes advantage of fully convolutional architecture to perform efficient, precise and volume-to-volume prediction. Notably, by recursively applying a single expansion rule, we construct our network in a novel self-similar fractal scheme and thus promote it in combining hierarchical clues for accurate segmentation. More importantly, we employ deep supervision mechanism to alleviate the vanishing gradients problem and improve the training efficiency of our network on small medical image dataset. We evaluated our method on the HVSMR 2016 Challenge dataset. Extensive experimental results demonstrated the superior performance of our method, ranking top in both two phases.-
dc.languageeng-
dc.publisherSpringer.-
dc.relation.ispartofReconstruction, Segmentation, and Analysis of Medical Images: First International Workshops, RAMBO 2016 and HVSMR 2016, Held in Conjunction with MICCAI 2016, Athens, Greece, October 17, 2016, Revised Selected Papers-
dc.relation.ispartofseriesLecture Notes in Computer Science ; 10129-
dc.title3D FractalNet: Dense volumetric segmentation for cardiovascular MRI volumes-
dc.typeConference_Paper-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1007/978-3-319-52280-7_10-
dc.identifier.scopuseid_2-s2.0-85011266714-
dc.identifier.spage103-
dc.identifier.epage110-
dc.identifier.eissn1611-3349-
dc.identifier.isiWOS:000425523400010-
dc.publisher.placeCham, Switzerland-

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