SpineParseNet: Spine Parsing for Volumetric MR Image by a Two-Stage Segmentation Framework With Semantic Image Representation

Spine parsing (i.e., multi-class segmentation of vertebrae and intervertebral discs (IVDs)) for volumetric magnetic resonance (MR) image plays a significant role in various spinal disease diagnoses and treatments of spine disorders, yet is still a challenge due to the inter-class similarity and intr...

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Published in:	IEEE transactions on medical imaging Vol. 40; no. 1; pp. 262 - 273
Main Authors:	Pang, Shumao, Pang, Chunlan, Zhao, Lei, Chen, Yangfan, Su, Zhihai, Zhou, Yujia, Huang, Meiyan, Yang, Wei, Lu, Hai, Feng, Qianjin
Format:	Journal Article
Language:	English
Published:	United States IEEE 01.01.2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	3D segmentation Computed tomography Deep learning graph convolution Graph representations Graphical representations Image processing Image representation Image segmentation Intervertebral discs Learning systems Magnetic resonance imaging Medical treatment Semantics Spine Three-dimensional displays Two dimensional displays Vertebrae
ISSN:	0278-0062, 1558-254X, 1558-254X
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Abstract	Spine parsing (i.e., multi-class segmentation of vertebrae and intervertebral discs (IVDs)) for volumetric magnetic resonance (MR) image plays a significant role in various spinal disease diagnoses and treatments of spine disorders, yet is still a challenge due to the inter-class similarity and intra-class variation of spine images. Existing fully convolutional network based methods failed to explicitly exploit the dependencies between different spinal structures. In this article, we propose a novel two-stage framework named SpineParseNet to achieve automated spine parsing for volumetric MR images. The SpineParseNet consists of a 3D graph convolutional segmentation network (GCSN) for 3D coarse segmentation and a 2D residual U-Net (ResUNet) for 2D segmentation refinement. In 3D GCSN, region pooling is employed to project the image representation to graph representation, in which each node representation denotes a specific spinal structure. The adjacency matrix of the graph is designed according to the connection of spinal structures. The graph representation is evolved by graph convolutions. Subsequently, the proposed region unpooling module re-projects the evolved graph representation to a semantic image representation, which facilitates the 3D GCSN to generate reliable coarse segmentation. Finally, the 2D ResUNet refines the segmentation. Experiments on T2-weighted volumetric MR images of 215 subjects show that SpineParseNet achieves impressive performance with mean Dice similarity coefficients of 87.32 ± 4.75%, 87.78 ± 4.64%, and 87.49 ± 3.81% for the segmentations of 10 vertebrae, 9 IVDs, and all 19 spinal structures respectively. The proposed method has great potential in clinical spinal disease diagnoses and treatments.
AbstractList	Spine parsing (i.e., multi-class segmentation of vertebrae and intervertebral discs (IVDs)) for volumetric magnetic resonance (MR) image plays a significant role in various spinal disease diagnoses and treatments of spine disorders, yet is still a challenge due to the inter-class similarity and intra-class variation of spine images. Existing fully convolutional network based methods failed to explicitly exploit the dependencies between different spinal structures. In this article, we propose a novel two-stage framework named SpineParseNet to achieve automated spine parsing for volumetric MR images. The SpineParseNet consists of a 3D graph convolutional segmentation network (GCSN) for 3D coarse segmentation and a 2D residual U-Net (ResUNet) for 2D segmentation refinement. In 3D GCSN, region pooling is employed to project the image representation to graph representation, in which each node representation denotes a specific spinal structure. The adjacency matrix of the graph is designed according to the connection of spinal structures. The graph representation is evolved by graph convolutions. Subsequently, the proposed region unpooling module re-projects the evolved graph representation to a semantic image representation, which facilitates the 3D GCSN to generate reliable coarse segmentation. Finally, the 2D ResUNet refines the segmentation. Experiments on T2-weighted volumetric MR images of 215 subjects show that SpineParseNet achieves impressive performance with mean Dice similarity coefficients of 87.32 ± 4.75%, 87.78 ± 4.64%, and 87.49 ± 3.81% for the segmentations of 10 vertebrae, 9 IVDs, and all 19 spinal structures respectively. The proposed method has great potential in clinical spinal disease diagnoses and treatments. Spine parsing (i.e., multi-class segmentation of vertebrae and intervertebral discs (IVDs)) for volumetric magnetic resonance (MR) image plays a significant role in various spinal disease diagnoses and treatments of spine disorders, yet is still a challenge due to the inter-class similarity and intra-class variation of spine images. Existing fully convolutional network based methods failed to explicitly exploit the dependencies between different spinal structures. In this article, we propose a novel two-stage framework named SpineParseNet to achieve automated spine parsing for volumetric MR images. The SpineParseNet consists of a 3D graph convolutional segmentation network (GCSN) for 3D coarse segmentation and a 2D residual U-Net (ResUNet) for 2D segmentation refinement. In 3D GCSN, region pooling is employed to project the image representation to graph representation, in which each node representation denotes a specific spinal structure. The adjacency matrix of the graph is designed according to the connection of spinal structures. The graph representation is evolved by graph convolutions. Subsequently, the proposed region unpooling module re-projects the evolved graph representation to a semantic image representation, which facilitates the 3D GCSN to generate reliable coarse segmentation. Finally, the 2D ResUNet refines the segmentation. Experiments on T2-weighted volumetric MR images of 215 subjects show that SpineParseNet achieves impressive performance with mean Dice similarity coefficients of 87.32 ± 4.75%, 87.78 ± 4.64%, and 87.49 ± 3.81% for the segmentations of 10 vertebrae, 9 IVDs, and all 19 spinal structures respectively. The proposed method has great potential in clinical spinal disease diagnoses and treatments.Spine parsing (i.e., multi-class segmentation of vertebrae and intervertebral discs (IVDs)) for volumetric magnetic resonance (MR) image plays a significant role in various spinal disease diagnoses and treatments of spine disorders, yet is still a challenge due to the inter-class similarity and intra-class variation of spine images. Existing fully convolutional network based methods failed to explicitly exploit the dependencies between different spinal structures. In this article, we propose a novel two-stage framework named SpineParseNet to achieve automated spine parsing for volumetric MR images. The SpineParseNet consists of a 3D graph convolutional segmentation network (GCSN) for 3D coarse segmentation and a 2D residual U-Net (ResUNet) for 2D segmentation refinement. In 3D GCSN, region pooling is employed to project the image representation to graph representation, in which each node representation denotes a specific spinal structure. The adjacency matrix of the graph is designed according to the connection of spinal structures. The graph representation is evolved by graph convolutions. Subsequently, the proposed region unpooling module re-projects the evolved graph representation to a semantic image representation, which facilitates the 3D GCSN to generate reliable coarse segmentation. Finally, the 2D ResUNet refines the segmentation. Experiments on T2-weighted volumetric MR images of 215 subjects show that SpineParseNet achieves impressive performance with mean Dice similarity coefficients of 87.32 ± 4.75%, 87.78 ± 4.64%, and 87.49 ± 3.81% for the segmentations of 10 vertebrae, 9 IVDs, and all 19 spinal structures respectively. The proposed method has great potential in clinical spinal disease diagnoses and treatments.
Author	Zhou, Yujia Pang, Chunlan Chen, Yangfan Pang, Shumao Feng, Qianjin Zhao, Lei Su, Zhihai Huang, Meiyan Yang, Wei Lu, Hai
Author_xml	– sequence: 1 givenname: Shumao orcidid: 0000-0003-0409-8562 surname: Pang fullname: Pang, Shumao email: pangshumao@126.com organization: Guangdong Provincial Key Laboratory of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, Guangzhou, China – sequence: 2 givenname: Chunlan surname: Pang fullname: Pang, Chunlan email: pangchl@sysucc.org.cn organization: Department of Nuclear Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China – sequence: 3 givenname: Lei surname: Zhao fullname: Zhao, Lei email: lei6730@smu.edu.cn organization: Guangdong Provincial Key Laboratory of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, Guangzhou, China – sequence: 4 givenname: Yangfan surname: Chen fullname: Chen, Yangfan email: cyangfan4@gmail.com organization: Guangdong Provincial Key Laboratory of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, Guangzhou, China – sequence: 5 givenname: Zhihai surname: Su fullname: Su, Zhihai email: 13265092954@163.com organization: Department of Spinal Surgery, The Fifth Affiliated Hospital, Sun Yat-sen University, Zhuhai, China – sequence: 6 givenname: Yujia surname: Zhou fullname: Zhou, Yujia email: zyj.shmily08@gmail.com organization: Guangdong Provincial Key Laboratory of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, Guangzhou, China – sequence: 7 givenname: Meiyan surname: Huang fullname: Huang, Meiyan email: huangmeiyan11@gmail.com organization: Guangdong Provincial Key Laboratory of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, Guangzhou, China – sequence: 8 givenname: Wei orcidid: 0000-0002-2161-3231 surname: Yang fullname: Yang, Wei email: weiyanggm@gmail.com organization: Guangdong Provincial Key Laboratory of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, Guangzhou, China – sequence: 9 givenname: Hai surname: Lu fullname: Lu, Hai email: hailu_nanfang@163.com organization: Department of Spinal Surgery, The Fifth Affiliated Hospital, Sun Yat-sen University, Zhuhai, China – sequence: 10 givenname: Qianjin orcidid: 0000-0001-8647-0596 surname: Feng fullname: Feng, Qianjin email: fengqj99@fimmu.com organization: Guangdong Provincial Key Laboratory of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, Guangzhou, China
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References	ref35 kingma (ref34) 2014 ref12 kipf (ref28) 2016; abs 1609 2907 ref15 chen (ref25) 2018 ref14 lee (ref33) 2017 ref31 ref30 ref11 sekuboyina (ref13) 2017 ref10 ref2 ref1 ref17 ronneberger (ref24) 2015 ref19 ref18 sekuboyina (ref16) 2017 çiçek (ref32) 2016 korez (ref9) 2015 ref23 ref26 ref20 ref22 ref21 ref27 ref29 ref8 ref7 ref4 ref3 ref6 ref5
References_xml	– ident: ref10 doi: 10.1109/TMI.2017.2667578 – start-page: 424 year: 2016 ident: ref32 article-title: 3D U-Net: Learning dense volumetric segmentation from sparse annotation publication-title: Proc Int Conf Med Image Comput Comput -Assist Intervent – ident: ref17 doi: 10.1109/COMPSAC.2019.00109 – ident: ref30 doi: 10.1109/CVPR.2017.195 – volume: abs 1609 2907 year: 2016 ident: ref28 article-title: Semi-supervised classification with graph convolutional networks publication-title: CoRR – ident: ref26 doi: 10.1162/neco.1997.9.8.1735 – ident: ref3 doi: 10.1007/978-3-319-14148-0_18 – ident: ref7 doi: 10.1109/TMI.2013.2244903 – ident: ref20 doi: 10.1109/JBHI.2020.2969084 – ident: ref21 doi: 10.1109/JBHI.2018.2872810 – ident: ref4 doi: 10.1007/978-3-319-41827-8_10 – ident: ref23 doi: 10.1109/CVPR.2016.90 – ident: ref31 doi: 10.1016/j.neuroimage.2006.01.015 – ident: ref19 doi: 10.1016/j.media.2019.02.005 – year: 2014 ident: ref34 article-title: Adam: A method for stochastic optimization publication-title: Proc 3rd Int Conf Learn Representations – ident: ref27 doi: 10.1016/j.media.2018.08.005 – start-page: 801 year: 2018 ident: ref25 article-title: Encoder-decoder with atrous separable convolution for semantic image segmentation publication-title: Proc Eur Conf Comput Vis (ECCV) – year: 2017 ident: ref16 article-title: A localisation-segmentation approach for multi-label annotation of lumbar vertebrae using deep nets publication-title: arXiv 1703 04347 – ident: ref15 doi: 10.1016/j.media.2018.01.004 – ident: ref6 doi: 10.1088/0031-9155/57/24/8357 – ident: ref29 doi: 10.1109/CVPR.2019.00763 – ident: ref1 doi: 10.1016/j.spinee.2019.01.001 – ident: ref22 doi: 10.1109/ACCESS.2019.2962608 – ident: ref14 doi: 10.1109/ISBI.2018.8363715 – ident: ref8 doi: 10.1016/j.media.2012.06.006 – ident: ref18 doi: 10.1109/ISBI.2019.8759413 – start-page: 117 year: 2015 ident: ref9 article-title: Deformable model-based segmentation of intervertebral discs from MR spine images by using the SSC descriptor publication-title: Proc Int Workshop Challenge Comput Methods Clin Appl Spine Imag – ident: ref12 doi: 10.1007/978-3-319-43775-0_34 – start-page: 108 year: 2017 ident: ref13 article-title: Attention-driven deep learning for pathological spine segmentation publication-title: Proc Int Workshop Challenge Comput Methods Clin Appl Musculoskeletal Imag – start-page: 234 year: 2015 ident: ref24 article-title: U-net: Convolutional networks for biomedical image segmentation publication-title: Proc Int Conf Med Image Comput Comput -Assist Intervent – ident: ref2 doi: 10.1006/cviu.1997.0608 – ident: ref5 doi: 10.1109/TBME.2012.2225833 – ident: ref35 doi: 10.1109/TMI.2019.2906727 – ident: ref11 doi: 10.1109/TMI.2015.2403285 – year: 2017 ident: ref33 article-title: Superhuman accuracy on the SNEMI3D connectomics challenge publication-title: arXiv 1706 00120 [cs]
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SubjectTerms	3D segmentation Computed tomography Deep learning graph convolution Graph representations Graphical representations Image processing Image representation Image segmentation Intervertebral discs Learning systems Magnetic resonance imaging Medical treatment Semantics Spine Three-dimensional displays Two dimensional displays Vertebrae
Title	SpineParseNet: Spine Parsing for Volumetric MR Image by a Two-Stage Segmentation Framework With Semantic Image Representation
URI	https://ieeexplore.ieee.org/document/9201093 https://www.ncbi.nlm.nih.gov/pubmed/32956047 https://www.proquest.com/docview/2474854428 https://www.proquest.com/docview/2444875235
Volume	40
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