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Conference Paper: 3D U-net with multi-level deep supervision: Fully automatic segmentation of proximal femur in 3D MR images

Title3D U-net with multi-level deep supervision: Fully automatic segmentation of proximal femur in 3D MR images
Authors
KeywordsMR images
Proximal femur
Deep learning
Segmentation
Issue Date2017
PublisherSpringer.
Citation
8th International Workshop on Machine Learning in Medical Imaging (MLMI 2017), Held in Conjunction with MICCAI 2017, Quebec City, QC, Canada, 10 September 2017. In Wang, Q, Shi, Y, Suk, H, Suzuki, K (Eds.), Machine Learning in Medical Imaging: 8th International Workshop, MLMI 2017, Held in Conjunction with MICCAI 2017, Quebec City, QC, Canada, September 10, 2017, Proceedings, p. 274-282. Cham, Switzerland: Springer, 2017 How to Cite?
AbstractThis paper addresses the problem of segmentation of proximal femur in 3D MR images. We propose a deeply supervised 3D U-net-like fully convolutional network for segmentation of proximal femur in 3D MR images. After training, our network can directly map a whole volumetric data to its volume-wise labels. Inspired by previous work, multi-level deep supervision is designed to alleviate the potential gradient vanishing problem during training. It is also used together with partial transfer learning to boost the training efficiency when only small set of labeled training data are available. The present method was validated on 20 3D MR images of femoroacetabular impingement patients. The experimental results demonstrate the efficacy of the present method.
Persistent Identifierhttp://hdl.handle.net/10722/299560
ISBN
ISSN
2023 SCImago Journal Rankings: 0.606
ISI Accession Number ID
Series/Report no.Lecture Notes in Computer Science ; 10541

 

DC FieldValueLanguage
dc.contributor.authorZeng, Guodong-
dc.contributor.authorYang, Xin-
dc.contributor.authorLi, Jing-
dc.contributor.authorYu, Lequan-
dc.contributor.authorHeng, Pheng Ann-
dc.contributor.authorZheng, Guoyan-
dc.date.accessioned2021-05-21T03:34:40Z-
dc.date.available2021-05-21T03:34:40Z-
dc.date.issued2017-
dc.identifier.citation8th International Workshop on Machine Learning in Medical Imaging (MLMI 2017), Held in Conjunction with MICCAI 2017, Quebec City, QC, Canada, 10 September 2017. In Wang, Q, Shi, Y, Suk, H, Suzuki, K (Eds.), Machine Learning in Medical Imaging: 8th International Workshop, MLMI 2017, Held in Conjunction with MICCAI 2017, Quebec City, QC, Canada, September 10, 2017, Proceedings, p. 274-282. Cham, Switzerland: Springer, 2017-
dc.identifier.isbn9783319673882-
dc.identifier.issn0302-9743-
dc.identifier.urihttp://hdl.handle.net/10722/299560-
dc.description.abstractThis paper addresses the problem of segmentation of proximal femur in 3D MR images. We propose a deeply supervised 3D U-net-like fully convolutional network for segmentation of proximal femur in 3D MR images. After training, our network can directly map a whole volumetric data to its volume-wise labels. Inspired by previous work, multi-level deep supervision is designed to alleviate the potential gradient vanishing problem during training. It is also used together with partial transfer learning to boost the training efficiency when only small set of labeled training data are available. The present method was validated on 20 3D MR images of femoroacetabular impingement patients. The experimental results demonstrate the efficacy of the present method.-
dc.languageeng-
dc.publisherSpringer.-
dc.relation.ispartofMachine Learning in Medical Imaging: 8th International Workshop, MLMI 2017, Held in Conjunction with MICCAI 2017, Quebec City, QC, Canada, September 10, 2017, Proceedings-
dc.relation.ispartofseriesLecture Notes in Computer Science ; 10541-
dc.subjectMR images-
dc.subjectProximal femur-
dc.subjectDeep learning-
dc.subjectSegmentation-
dc.title3D U-net with multi-level deep supervision: Fully automatic segmentation of proximal femur in 3D MR images-
dc.typeConference_Paper-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1007/978-3-319-67389-9_32-
dc.identifier.scopuseid_2-s2.0-85029720982-
dc.identifier.spage274-
dc.identifier.epage282-
dc.identifier.eissn1611-3349-
dc.identifier.isiWOS:000463270400032-
dc.publisher.placeCham, Switzerland-

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