Multi-scale masked 3-D U-Net for brain tumor segmentation

Xu, Y; Gong, M; Fu, H; Tao, D; Zhang, K; Batmanghelich, K

Multi-scale masked 3-D U-Net for brain tumor segmentation

Xu, Y Gong, M Fu, H Tao, D

Zhang, K Batmanghelich, K

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Publisher:: Springer International Publishing
Publication Type:: Conference Proceeding
Citation:: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2019, 11384 LNCS, pp. 222-233
Issue Date:: 2019-01-01

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Field	Value	Language
dc.contributor.author	Xu, Y
dc.contributor.author	Gong, M
dc.contributor.author	Fu, H
dc.contributor.author	Tao, D https://orcid.org/0000-0001-7225-5449
dc.contributor.author	Zhang, K
dc.contributor.author	Batmanghelich, K
dc.date.accessioned	2020-06-16T00:32:34Z
dc.date.available	2020-06-16T00:32:34Z
dc.date.issued	2019-01-01
dc.identifier.citation	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2019, 11384 LNCS, pp. 222-233
dc.identifier.isbn	9783030117252
dc.identifier.issn	0302-9743
dc.identifier.issn	1611-3349
dc.identifier.uri	http://hdl.handle.net/10453/141438
dc.description.abstract	© Springer Nature Switzerland AG 2019. The brain tumor segmentation task aims to classify sub-regions into peritumoral edema, necrotic core, enhancing and non-enhancing tumor core using multimodal MRI scans. This task is very challenging due to its intrinsic high heterogeneity of appearance and shape. Recently, with the development of deep models and computing resources, deep convolutional neural networks have shown their effectiveness on brain tumor segmentation from 3D MRI cans, obtaining the top performance in the MICCAI BraTS challenge 2017. In this paper we further boost the performance of brain tumor segmentation by proposing a multi-scale masked 3D U-Net which captures multi-scale information by stacking multi-scale images as inputs and incorporating a 3-D Atrous Spatial Pyramid Pooling (ASPP) layer. To filter noisy results for tumor core (TC) and enhancing tumor (ET), we train the TC and ET segmentation networks from the bounding box for whole tumor (WT) and TC, respectively. On the BraTS 2018 validation set, our method achieved average Dice scores of 0.8094, 0.9034, 0.8319 for ET, WT and TC, respectively. On the BraTS 2018 test set, our method achieved 0.7690, 0.8711, and 0.7792 dice scores for ET, WT and TC, respectively. Especially, our multi-scale masked 3D network achieved very promising results enhancing tumor (ET), which is hardest to segment due to small scales and irregular shapes.
dc.language	en
dc.publisher	Springer International Publishing
dc.relation.ispartof	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.relation.isbasedon	10.1007/978-3-030-11726-9_20
dc.rights	info:eu-repo/semantics/restrictedAccess
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	Multi-scale masked 3-D U-Net for brain tumor segmentation
dc.type	Conference Proceeding
utslib.citation.volume	11384 LNCS
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	closed_access	*
dc.date.updated	2020-06-16T00:32:32Z
pubs.publication-status	Published
pubs.volume	11384 LNCS

Abstract:

© Springer Nature Switzerland AG 2019. The brain tumor segmentation task aims to classify sub-regions into peritumoral edema, necrotic core, enhancing and non-enhancing tumor core using multimodal MRI scans. This task is very challenging due to its intrinsic high heterogeneity of appearance and shape. Recently, with the development of deep models and computing resources, deep convolutional neural networks have shown their effectiveness on brain tumor segmentation from 3D MRI cans, obtaining the top performance in the MICCAI BraTS challenge 2017. In this paper we further boost the performance of brain tumor segmentation by proposing a multi-scale masked 3D U-Net which captures multi-scale information by stacking multi-scale images as inputs and incorporating a 3-D Atrous Spatial Pyramid Pooling (ASPP) layer. To filter noisy results for tumor core (TC) and enhancing tumor (ET), we train the TC and ET segmentation networks from the bounding box for whole tumor (WT) and TC, respectively. On the BraTS 2018 validation set, our method achieved average Dice scores of 0.8094, 0.9034, 0.8319 for ET, WT and TC, respectively. On the BraTS 2018 test set, our method achieved 0.7690, 0.8711, and 0.7792 dice scores for ET, WT and TC, respectively. Especially, our multi-scale masked 3D network achieved very promising results enhancing tumor (ET), which is hardest to segment due to small scales and irregular shapes.

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http://hdl.handle.net/10453/141438