BX2S-Net: Learning to reconstruct 3D spinal structures from bi-planar X-ray images

Grasping good understanding of the weight-bearing spatial structure of the spine of a human subject in a standing position is critical for the treatment of spinal disorders. Such disorders are commonly diagnosed via 2D X-ray imaging of the human subject in a standing position. However, 3D reconstruc...

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Vydáno v:	Computers in biology and medicine Ročník 154; s. 106615
Hlavní autoři:	Chen, Zheye, Guo, Lijun, Zhang, Rong, Fang, Zhongding, He, Xiuchao, Wang, Jianhua
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	United States Elsevier Ltd 01.03.2023 Elsevier Limited
Témata:	3D reconstruction Artificial neural networks Bi-planar X-ray images Coders Data integration Decoders Decoding Deep learning Disorders Entropy Feature maps Formability Human subjects Humans Image Processing, Computer-Assisted - methods Image reconstruction Imaging, Three-Dimensional - methods Internal Medicine Neural networks Neural Networks, Computer Other Progressive decoding process Semantics Spine Spine - diagnostic imaging Vertebrae X ray imagery X-Rays Deep learning 3D reconstruction Bi-planar X-ray images Spine Progressive decoding process
ISSN:	0010-4825, 1879-0534, 1879-0534
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Abstract	Grasping good understanding of the weight-bearing spatial structure of the spine of a human subject in a standing position is critical for the treatment of spinal disorders. Such disorders are commonly diagnosed via 2D X-ray imaging of the human subject in a standing position. However, 3D reconstruction techniques based on bi-planar X-ray imaging can enable better exploration and analysis of the spinal structure. In particular, compared to earlier deformable modeling approaches, the recently-developed deep-learning-based 3D reconstruction methods exhibit higher efficiency and generalizability. But these methods usually employ simple architectures with 2D encoders and 3D decoders. Thus, these methods have several drawbacks, namely, the existence of a semantic gap between dimensionally-inconsistent feature maps, the difficulty of jointly handling multi-view inputs, and the information source limitations for the decoding process. In order to better assist clinicians and tackle these problems, we propose a novel convolutional neural network framework, which we call BX2S-Net, to effectively achieve 3D spine reconstruction based on bi-planar X-ray images. In particular, a dimensionally-consistent encoder–decoder architecture is designed in conjunction with a dimensionality enhancement method in order to reduce the semantic gap between feature maps and achieve information fusion for multi-view inputs. A feature-guided progressive decoding process is developed on the decoder side, where a full-scale feature attention guidance (FFAG) module is introduced to efficiently aggregate image features and guide the decoding process at each level. In addition, a class augmentation method and a spatially-weighted cross-entropy loss function are used for network training with improved reconstruction quality for the vertebral edge region. The experimental results demonstrate the effectiveness of our model in reconstructing high-quality 3D spinal structures from bi-planar X-ray images. The code is available at https://github.com/NBU-CVMI/bx2s-net. •A deep learning method is proposed to achieve 3D spine reconstruction.•Multi-view inputs are processed via a dimensionally-consistent network structure.•A feature-guided progressive decoding process is developed in the network.•Several strategies are introduced to enhance the reconstruction of edge regions.
AbstractList	Grasping good understanding of the weight-bearing spatial structure of the spine of a human subject in a standing position is critical for the treatment of spinal disorders. Such disorders are commonly diagnosed via 2D X-ray imaging of the human subject in a standing position. However, 3D reconstruction techniques based on bi-planar X-ray imaging can enable better exploration and analysis of the spinal structure. In particular, compared to earlier deformable modeling approaches, the recently-developed deep-learning-based 3D reconstruction methods exhibit higher efficiency and generalizability. But these methods usually employ simple architectures with 2D encoders and 3D decoders. Thus, these methods have several drawbacks, namely, the existence of a semantic gap between dimensionally-inconsistent feature maps, the difficulty of jointly handling multi-view inputs, and the information source limitations for the decoding process. In order to better assist clinicians and tackle these problems, we propose a novel convolutional neural network framework, which we call BX2S-Net, to effectively achieve 3D spine reconstruction based on bi-planar X-ray images. In particular, a dimensionally-consistent encoder-decoder architecture is designed in conjunction with a dimensionality enhancement method in order to reduce the semantic gap between feature maps and achieve information fusion for multi-view inputs. A feature-guided progressive decoding process is developed on the decoder side, where a full-scale feature attention guidance (FFAG) module is introduced to efficiently aggregate image features and guide the decoding process at each level. In addition, a class augmentation method and a spatially-weighted cross-entropy loss function are used for network training with improved reconstruction quality for the vertebral edge region. The experimental results demonstrate the effectiveness of our model in reconstructing high-quality 3D spinal structures from bi-planar X-ray images. The code is available at https://github.com/NBU-CVMI/bx2s-net.Grasping good understanding of the weight-bearing spatial structure of the spine of a human subject in a standing position is critical for the treatment of spinal disorders. Such disorders are commonly diagnosed via 2D X-ray imaging of the human subject in a standing position. However, 3D reconstruction techniques based on bi-planar X-ray imaging can enable better exploration and analysis of the spinal structure. In particular, compared to earlier deformable modeling approaches, the recently-developed deep-learning-based 3D reconstruction methods exhibit higher efficiency and generalizability. But these methods usually employ simple architectures with 2D encoders and 3D decoders. Thus, these methods have several drawbacks, namely, the existence of a semantic gap between dimensionally-inconsistent feature maps, the difficulty of jointly handling multi-view inputs, and the information source limitations for the decoding process. In order to better assist clinicians and tackle these problems, we propose a novel convolutional neural network framework, which we call BX2S-Net, to effectively achieve 3D spine reconstruction based on bi-planar X-ray images. In particular, a dimensionally-consistent encoder-decoder architecture is designed in conjunction with a dimensionality enhancement method in order to reduce the semantic gap between feature maps and achieve information fusion for multi-view inputs. A feature-guided progressive decoding process is developed on the decoder side, where a full-scale feature attention guidance (FFAG) module is introduced to efficiently aggregate image features and guide the decoding process at each level. In addition, a class augmentation method and a spatially-weighted cross-entropy loss function are used for network training with improved reconstruction quality for the vertebral edge region. The experimental results demonstrate the effectiveness of our model in reconstructing high-quality 3D spinal structures from bi-planar X-ray images. The code is available at https://github.com/NBU-CVMI/bx2s-net. AbstractGrasping good understanding of the weight-bearing spatial structure of the spine of a human subject in a standing position is critical for the treatment of spinal disorders. Such disorders are commonly diagnosed via 2D X-ray imaging of the human subject in a standing position. However, 3D reconstruction techniques based on bi-planar X-ray imaging can enable better exploration and analysis of the spinal structure. In particular, compared to earlier deformable modeling approaches, the recently-developed deep-learning-based 3D reconstruction methods exhibit higher efficiency and generalizability. But these methods usually employ simple architectures with 2D encoders and 3D decoders. Thus, these methods have several drawbacks, namely, the existence of a semantic gap between dimensionally-inconsistent feature maps, the difficulty of jointly handling multi-view inputs, and the information source limitations for the decoding process. In order to better assist clinicians and tackle these problems, we propose a novel convolutional neural network framework, which we call BX2S-Net, to effectively achieve 3D spine reconstruction based on bi-planar X-ray images. In particular, a dimensionally-consistent encoder–decoder architecture is designed in conjunction with a dimensionality enhancement method in order to reduce the semantic gap between feature maps and achieve information fusion for multi-view inputs. A feature-guided progressive decoding process is developed on the decoder side, where a full-scale feature attention guidance (FFAG) module is introduced to efficiently aggregate image features and guide the decoding process at each level. In addition, a class augmentation method and a spatially-weighted cross-entropy loss function are used for network training with improved reconstruction quality for the vertebral edge region. The experimental results demonstrate the effectiveness of our model in reconstructing high-quality 3D spinal structures from bi-planar X-ray images. The code is available at https://github.com/NBU-CVMI/bx2s-net. Grasping good understanding of the weight-bearing spatial structure of the spine of a human subject in a standing position is critical for the treatment of spinal disorders. Such disorders are commonly diagnosed via 2D X-ray imaging of the human subject in a standing position. However, 3D reconstruction techniques based on bi-planar X-ray imaging can enable better exploration and analysis of the spinal structure. In particular, compared to earlier deformable modeling approaches, the recently-developed deep-learning-based 3D reconstruction methods exhibit higher efficiency and generalizability. But these methods usually employ simple architectures with 2D encoders and 3D decoders. Thus, these methods have several drawbacks, namely, the existence of a semantic gap between dimensionally-inconsistent feature maps, the difficulty of jointly handling multi-view inputs, and the information source limitations for the decoding process. In order to better assist clinicians and tackle these problems, we propose a novel convolutional neural network framework, which we call BX2S-Net, to effectively achieve 3D spine reconstruction based on bi-planar X-ray images. In particular, a dimensionally-consistent encoder–decoder architecture is designed in conjunction with a dimensionality enhancement method in order to reduce the semantic gap between feature maps and achieve information fusion for multi-view inputs. A feature-guided progressive decoding process is developed on the decoder side, where a full-scale feature attention guidance (FFAG) module is introduced to efficiently aggregate image features and guide the decoding process at each level. In addition, a class augmentation method and a spatially-weighted cross-entropy loss function are used for network training with improved reconstruction quality for the vertebral edge region. The experimental results demonstrate the effectiveness of our model in reconstructing high-quality 3D spinal structures from bi-planar X-ray images. The code is available at https://github.com/NBU-CVMI/bx2s-net. Grasping good understanding of the weight-bearing spatial structure of the spine of a human subject in a standing position is critical for the treatment of spinal disorders. Such disorders are commonly diagnosed via 2D X-ray imaging of the human subject in a standing position. However, 3D reconstruction techniques based on bi-planar X-ray imaging can enable better exploration and analysis of the spinal structure. In particular, compared to earlier deformable modeling approaches, the recently-developed deep-learning-based 3D reconstruction methods exhibit higher efficiency and generalizability. But these methods usually employ simple architectures with 2D encoders and 3D decoders. Thus, these methods have several drawbacks, namely, the existence of a semantic gap between dimensionally-inconsistent feature maps, the difficulty of jointly handling multi-view inputs, and the information source limitations for the decoding process. In order to better assist clinicians and tackle these problems, we propose a novel convolutional neural network framework, which we call BX2S-Net, to effectively achieve 3D spine reconstruction based on bi-planar X-ray images. In particular, a dimensionally-consistent encoder–decoder architecture is designed in conjunction with a dimensionality enhancement method in order to reduce the semantic gap between feature maps and achieve information fusion for multi-view inputs. A feature-guided progressive decoding process is developed on the decoder side, where a full-scale feature attention guidance (FFAG) module is introduced to efficiently aggregate image features and guide the decoding process at each level. In addition, a class augmentation method and a spatially-weighted cross-entropy loss function are used for network training with improved reconstruction quality for the vertebral edge region. The experimental results demonstrate the effectiveness of our model in reconstructing high-quality 3D spinal structures from bi-planar X-ray images. The code is available at https://github.com/NBU-CVMI/bx2s-net. •A deep learning method is proposed to achieve 3D spine reconstruction.•Multi-view inputs are processed via a dimensionally-consistent network structure.•A feature-guided progressive decoding process is developed in the network.•Several strategies are introduced to enhance the reconstruction of edge regions.
ArticleNumber	106615
Author	Fang, Zhongding He, Xiuchao Zhang, Rong Guo, Lijun Wang, Jianhua Chen, Zheye
Author_xml	– sequence: 1 givenname: Zheye surname: Chen fullname: Chen, Zheye email: yingwenn@foxmail.com organization: Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo Zhejiang 315000, China – sequence: 2 givenname: Lijun orcidid: 0000-0002-6133-9564 surname: Guo fullname: Guo, Lijun email: guolijun@nbu.edu.cn organization: Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo Zhejiang 315000, China – sequence: 3 givenname: Rong surname: Zhang fullname: Zhang, Rong email: zhangrong@nbu.edu.cn organization: Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo Zhejiang 315000, China – sequence: 4 givenname: Zhongding orcidid: 0000-0003-3584-0010 surname: Fang fullname: Fang, Zhongding email: joongky@foxmail.com organization: Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo Zhejiang 315000, China – sequence: 5 givenname: Xiuchao surname: He fullname: He, Xiuchao email: hexiuchaoxctmr@163.com organization: Department of Radiology, The Affiliated Hospital of Medicine School of Ningbo University, Ningbo Zhejiang 315000, China – sequence: 6 givenname: Jianhua surname: Wang fullname: Wang, Jianhua email: woxingw@sina.com organization: Department of Radiology, The Affiliated Hospital of Medicine School of Ningbo University, Ningbo Zhejiang 315000, China
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Cites_doi	10.1016/j.apergo.2021.103491 10.1109/ICCV.2019.00278 10.1088/0031-9155/45/10/305 10.1007/s11548-010-0515-7 10.1016/j.compbiomed.2021.105070 10.1016/j.compbiomed.2022.105683 10.1109/RBME.2018.2876450 10.1007/s00586-021-06842-z 10.1007/s11042-020-09722-8 10.1007/978-3-030-01252-6_4 10.1016/j.compbiomed.2022.105347 10.1007/s11548-014-1121-x 10.1016/j.bea.2021.100023 10.1016/S0895-6111(03)00019-3 10.1016/j.compbiomed.2020.103766 10.1109/CVPR.2018.00207 10.1016/j.jelekin.2022.102640 10.1016/j.cag.2020.12.004 10.1016/j.joca.2020.12.003 10.1038/s41551-019-0466-4 10.7717/peerj.8397 10.1016/j.aej.2020.10.046 10.1109/CVPR.2016.445 10.1109/TBME.2013.2246788 10.4310/CIS.2020.v20.n4.a1 10.3390/s20185097 10.3390/ijerph18158152 10.1016/j.compbiomed.2020.104097 10.1016/j.media.2021.102166 10.1302/0301-620X.104B8.BJJ-2021-1638.R1 10.1016/j.medengphy.2009.01.003 10.1016/j.compbiomed.2021.104745 10.1016/j.compbiomed.2021.104268 10.1109/CVPR.2018.00745 10.1109/CVPR.2019.00352 10.1016/j.ijotn.2022.100921 10.1109/CVPR.2019.01087
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Keywords	Deep learning 3D reconstruction Bi-planar X-ray images Spine Progressive decoding process
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References	Zheng, Nolte, Ferguson (b16) 2011; 6 Wu, Zhang, Xue, Freeman, Tenenbaum (b37) 2016; 29 Muzaffarovna (b6) 2021; 11 C.R. Qi, H. Su, K. Mo, L.J. Guibas, Pointnet: Deep learning on point sets for 3D classification and segmentation, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017, pp. 652–660. Ahrend, Noser, Shanmugam, Burr, Kamer, Kamarul, Hügli, Nagy, Richards, Gueorguiev-Rüegg (b26) 2020; 20 Shen, Zhao, Xing (b40) 2019; 3 Sekuboyina, Husseini, Bayat, Löffler, Liebl, Li, Tetteh, Kukačka, Payer, Štern (b45) 2021; 73 Torén, Diarbakerli (b5) 2022 Alalwan, Abozeid, ElHabshy, Alzahrani (b33) 2021; 60 M. Tatarchenko, S.R. Richter, R. Ranftl, Z. Li, V. Koltun, T. Brox, What do single-view 3d reconstruction networks learn?, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019, pp. 3405–3414. Choy, Xu, Gwak, Chen, Savarese (b24) 2016 Grant, Diamond, Pizzolato, Killen, Devaprakash, Kelly, Maharaj, Saxby (b27) 2020; 8 J. Hu, L. Shen, G. Sun, Squeeze-and-excitation networks, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018, pp. 7132–7141. Zhou, He, Shi, Du, Chen (b31) 2020; 121 Clogenson, Duff, Luethi, Levivier, Meuli, Baur, Henein (b18) 2015; 10 Sung, Chae, Lee, Kim, Kwon, Lee, Moon, Lee, Lee (b1) 2021; 18 Seitz, Curless, Diebel, Scharstein, Szeliski (b13) 2006 Reyneke, Lüthi, Burdin, Douglas, Vetter, Mutsvangwa (b20) 2018; 12 H. Xie, H. Yao, X. Sun, S. Zhou, S. Zhang, Pix2Vox: Context-aware 3D reconstruction from single and multi-view images, in: Proceedings of the IEEE/CVF International Conference on Computer Vision, 2019, pp. 2690–2698. Kaya, Erbahçeci, Alkan, Kocaman, Büyükturan, Canlı, Büyükturan (b8) 2022; 62 Ogawa, Kisohara, Yamamoto, Shibata, Ojio, Mochizuki, Tatsuta, Iwasaki, Shibamoto (b35) 2022 Singh, Kucukgoz, Murphy, Xiong, Steel, Obara (b36) 2022; 140 Humbert, De Guise, Aubert, Godbout, Skalli (b19) 2009; 31 Zhang, Lv, Shi, Wang, Guo, Zhang, Li (b15) 2013; 60 Fu, Peng, He, Zhang (b23) 2021; 80 Chen, Yang, Wei, Heidari, Zheng, Li, Chen, Hu, Zhou, Guan (b21) 2022 Milickovic, Baltas, Giannouli, Lahanas, Zamboglou (b44) 2000; 45 Yu, Han, Li, Wei, Jiang, Chen, Yu (b29) 2022; 144 S.R. Richter, S. Roth, Matryoshka networks: Predicting 3d geometry via nested shape layers, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018, pp. 1936–1944. Jin, Jiang, Cai (b22) 2020; 20 Ng, Claus, Izatt, Pivonka, Tucker (b7) 2022 Skals, Bláfoss, de Zee, Andersen, Andersen (b11) 2021; 96 Singh, Wang, Gupta, Goli, Padmanabhan, Gulyás (b30) 2020; 20 Warren, Hey, Mazzoleni (b10) 2021; 2 Labrom, Izatt, Claus, Little (b2) 2021; 30 Marya, Tambe, Millner, Tsirikos (b4) 2022; 104 Qayyum, Lalande, Meriaudeau (b32) 2020; 127 X. Ying, H. Guo, K. Ma, J. Wu, Z. Weng, Y. Zheng, X2CT-GAN: reconstructing CT from biplanar X-rays with generative adversarial networks, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019, pp. 10619–10628. Essex, Bruce, Dibley, Newton, Thompson, Swaine, Dibley (b3) 2022 J.L. Schonberger, J.-M. Frahm, Structure-from-motion revisited, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 4104–4113. van Buuren, Arden, Bierma-Zeinstra, Bramer, Casartelli, Felson, Jones, Lane, Lindner, Maffiuletti (b28) 2021; 29 Deng, Wang, Hui, Li, Li, Luo, Sun, Quan, Yang, Hao (b46) 2021 Fahim, Amin, Zarif (b14) 2021; 94 Benameur, Mignotte, Parent, Labelle, Skalli, de Guise (b17) 2003; 27 Martin, Sciolla, Sdika, Quétin, Delachartre (b34) 2021; 131 N. Wang, Y. Zhang, Z. Li, Y. Fu, W. Liu, Y.-G. Jiang, Pixel2mesh: Generating 3d mesh models from single rgb images, in: Proceedings of the European Conference on Computer Vision, ECCV, 2018, pp. 52–67. Zhang, Chon, Zhang, Baker, Gu (b9) 2021; 136 Benameur (10.1016/j.compbiomed.2023.106615_b17) 2003; 27 Grant (10.1016/j.compbiomed.2023.106615_b27) 2020; 8 Sekuboyina (10.1016/j.compbiomed.2023.106615_b45) 2021; 73 Martin (10.1016/j.compbiomed.2023.106615_b34) 2021; 131 Singh (10.1016/j.compbiomed.2023.106615_b30) 2020; 20 10.1016/j.compbiomed.2023.106615_b38 Zhang (10.1016/j.compbiomed.2023.106615_b9) 2021; 136 Fu (10.1016/j.compbiomed.2023.106615_b23) 2021; 80 Alalwan (10.1016/j.compbiomed.2023.106615_b33) 2021; 60 Shen (10.1016/j.compbiomed.2023.106615_b40) 2019; 3 Kaya (10.1016/j.compbiomed.2023.106615_b8) 2022; 62 10.1016/j.compbiomed.2023.106615_b12 Zhou (10.1016/j.compbiomed.2023.106615_b31) 2020; 121 Wu (10.1016/j.compbiomed.2023.106615_b37) 2016; 29 Marya (10.1016/j.compbiomed.2023.106615_b4) 2022; 104 Yu (10.1016/j.compbiomed.2023.106615_b29) 2022; 144 Torén (10.1016/j.compbiomed.2023.106615_b5) 2022 Seitz (10.1016/j.compbiomed.2023.106615_b13) 2006 Choy (10.1016/j.compbiomed.2023.106615_b24) 2016 van Buuren (10.1016/j.compbiomed.2023.106615_b28) 2021; 29 Muzaffarovna (10.1016/j.compbiomed.2023.106615_b6) 2021; 11 Ng (10.1016/j.compbiomed.2023.106615_b7) 2022 10.1016/j.compbiomed.2023.106615_b41 Zheng (10.1016/j.compbiomed.2023.106615_b16) 2011; 6 Milickovic (10.1016/j.compbiomed.2023.106615_b44) 2000; 45 Ogawa (10.1016/j.compbiomed.2023.106615_b35) 2022 Deng (10.1016/j.compbiomed.2023.106615_b46) 2021 Essex (10.1016/j.compbiomed.2023.106615_b3) 2022 Chen (10.1016/j.compbiomed.2023.106615_b21) 2022 10.1016/j.compbiomed.2023.106615_b25 10.1016/j.compbiomed.2023.106615_b47 10.1016/j.compbiomed.2023.106615_b43 Fahim (10.1016/j.compbiomed.2023.106615_b14) 2021; 94 10.1016/j.compbiomed.2023.106615_b42 Clogenson (10.1016/j.compbiomed.2023.106615_b18) 2015; 10 Qayyum (10.1016/j.compbiomed.2023.106615_b32) 2020; 127 Zhang (10.1016/j.compbiomed.2023.106615_b15) 2013; 60 Sung (10.1016/j.compbiomed.2023.106615_b1) 2021; 18 Singh (10.1016/j.compbiomed.2023.106615_b36) 2022; 140 10.1016/j.compbiomed.2023.106615_b39 Skals (10.1016/j.compbiomed.2023.106615_b11) 2021; 96 Labrom (10.1016/j.compbiomed.2023.106615_b2) 2021; 30 Jin (10.1016/j.compbiomed.2023.106615_b22) 2020; 20 Warren (10.1016/j.compbiomed.2023.106615_b10) 2021; 2 Reyneke (10.1016/j.compbiomed.2023.106615_b20) 2018; 12 Humbert (10.1016/j.compbiomed.2023.106615_b19) 2009; 31 Ahrend (10.1016/j.compbiomed.2023.106615_b26) 2020; 20
References_xml	– volume: 121 year: 2020 ident: b31 article-title: 3D dense connectivity network with atrous convolutional feature pyramid for brain tumor segmentation in magnetic resonance imaging of human heads publication-title: Comput. Biol. Med. – volume: 60 start-page: 1231 year: 2021 end-page: 1239 ident: b33 article-title: Efficient 3D deep learning model for medical image semantic segmentation publication-title: Alex. Eng. J. – volume: 6 start-page: 351 year: 2011 end-page: 366 ident: b16 article-title: Scaled, patient-specific 3D vertebral model reconstruction based on 2D lateral fluoroscopy publication-title: Int. J. Comput. Assist. Radiol. Surg. – reference: J.L. Schonberger, J.-M. Frahm, Structure-from-motion revisited, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 4104–4113. – volume: 20 start-page: 100 year: 2020 end-page: 106 ident: b26 article-title: Development of generic Asian pelvic bone models using CT-based 3D statistical modelling publication-title: J. Orthop. Transl. – start-page: 519 year: 2006 end-page: 528 ident: b13 article-title: A comparison and evaluation of multi-view stereo reconstruction algorithms publication-title: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Vol. 1 – volume: 20 start-page: 389 year: 2020 end-page: 413 ident: b22 article-title: 3D reconstruction using deep learning: a survey publication-title: Commun. Inf. Syst. – volume: 62 year: 2022 ident: b8 article-title: Factors influencing of quality of life in adolescent idiopathic scoliosis publication-title: Musculoskelet. Sci. Pract. – volume: 144 year: 2022 ident: b29 article-title: Adaptive soft erasure with edge self-attention for weakly supervised semantic segmentation: thyroid ultrasound image case study publication-title: Comput. Biol. Med. – volume: 45 start-page: 2787 year: 2000 ident: b44 article-title: CT imaging based digitally reconstructed radiographs and their application in brachytherapy publication-title: Phys. Med. Biol. – volume: 10 start-page: 1097 year: 2015 end-page: 1107 ident: b18 article-title: A statistical shape model of the human second cervical vertebra publication-title: Int. J. Comput. Assist. Radiol. Surg. – year: 2022 ident: b7 article-title: Is spinal neuromuscular function asymmetrical in adolescents with idiopathic scoliosis compared to those without scoliosis?: A narrative review of surface EMG studies publication-title: J. Electromyography Kinesiol. – volume: 136 year: 2021 ident: b9 article-title: Finite element analysis of the lumbar spine in adolescent idiopathic scoliosis subjected to different loads publication-title: Comput. Biol. Med. – start-page: 628 year: 2016 end-page: 644 ident: b24 article-title: 3D-R2N2: A unified approach for single and multi-view 3D object reconstruction publication-title: European Conference on Computer Vision – volume: 3 start-page: 880 year: 2019 end-page: 888 ident: b40 article-title: Patient-specific reconstruction of volumetric computed tomography images from a single projection view via deep learning publication-title: Nat. Biomed. Eng. – volume: 18 start-page: 8152 year: 2021 ident: b1 article-title: Incidence and surgery rate of idiopathic scoliosis: a nationwide database study publication-title: Int. J. Environ. Res. Public Health – year: 2021 ident: b46 article-title: CTSpine1K: A large-scale dataset for spinal vertebrae segmentation in computed tomography – volume: 29 start-page: 607 year: 2021 end-page: 618 ident: b28 article-title: Statistical shape modeling of the hip and the association with hip osteoarthritis: a systematic review publication-title: Osteoarthr. Cartil. – reference: X. Ying, H. Guo, K. Ma, J. Wu, Z. Weng, Y. Zheng, X2CT-GAN: reconstructing CT from biplanar X-rays with generative adversarial networks, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019, pp. 10619–10628. – volume: 11 start-page: 359 year: 2021 end-page: 361 ident: b6 article-title: Morphometric changes in the parameters of physical development of children with scoliosis publication-title: Acad. Int. Multidiscip. Res. J. – year: 2022 ident: b35 article-title: Utility of unsupervised deep learning using a 3D variational autoencoder in detecting inner ear abnormalities on CT images publication-title: Comput. Biol. Med. – reference: J. Hu, L. Shen, G. Sun, Squeeze-and-excitation networks, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018, pp. 7132–7141. – volume: 60 start-page: 1954 year: 2013 end-page: 1964 ident: b15 article-title: 3-D reconstruction of the spine from biplanar radiographs based on contour matching using the hough transform publication-title: IEEE Trans. Biomed. Eng. – volume: 20 start-page: 5097 year: 2020 ident: b30 article-title: 3D deep learning on medical images: a review publication-title: Sensors – volume: 30 start-page: 1823 year: 2021 end-page: 1834 ident: b2 article-title: Adolescent idiopathic scoliosis 3D vertebral morphology, progression and nomenclature: a current concepts review publication-title: Eur. Spine J. – volume: 8 year: 2020 ident: b27 article-title: Development and validation of statistical shape models of the primary functional bone segments of the foot publication-title: PeerJ – volume: 127 year: 2020 ident: b32 article-title: Automatic segmentation of tumors and affected organs in the abdomen using a 3D hybrid model for computed tomography imaging publication-title: Comput. Biol. Med. – volume: 31 start-page: 681 year: 2009 end-page: 687 ident: b19 article-title: 3D reconstruction of the spine from biplanar X-rays using parametric models based on transversal and longitudinal inferences publication-title: Med. Eng. Phys. – volume: 73 year: 2021 ident: b45 article-title: VerSe: A vertebrae labelling and segmentation benchmark for multi-detector CT images publication-title: Med. Image Anal. – volume: 131 year: 2021 ident: b34 article-title: Automatic segmentation and location learning of neonatal cerebral ventricles in 3D ultrasound data combining CNN and CPPN publication-title: Comput. Biol. Med. – year: 2022 ident: b3 article-title: A systematic scoping review and textual narrative synthesis of the qualitative evidence related to adolescent idiopathic scoliosis publication-title: Int. J. Orthop. Trauma Nurs. – volume: 140 year: 2022 ident: b36 article-title: Benchmarking automated detection of the retinal external limiting membrane in a 3D spectral domain optical coherence tomography image dataset of full thickness macular holes publication-title: Comput. Biol. Med. – start-page: 1 year: 2022 end-page: 7 ident: b5 article-title: Health-related quality of life in adolescents with idiopathic scoliosis: a cross-sectional study including healthy controls publication-title: Eur. Spine J. – reference: M. Tatarchenko, S.R. Richter, R. Ranftl, Z. Li, V. Koltun, T. Brox, What do single-view 3d reconstruction networks learn?, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019, pp. 3405–3414. – reference: H. Xie, H. Yao, X. Sun, S. Zhou, S. Zhang, Pix2Vox: Context-aware 3D reconstruction from single and multi-view images, in: Proceedings of the IEEE/CVF International Conference on Computer Vision, 2019, pp. 2690–2698. – volume: 12 start-page: 269 year: 2018 end-page: 286 ident: b20 article-title: Review of 2-D/3-D reconstruction using statistical shape and intensity models and X-ray image synthesis: Toward a unified framework publication-title: IEEE Rev. Biomed. Eng. – reference: S.R. Richter, S. Roth, Matryoshka networks: Predicting 3d geometry via nested shape layers, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018, pp. 1936–1944. – volume: 27 start-page: 321 year: 2003 end-page: 337 ident: b17 article-title: 3D/2D registration and segmentation of scoliotic vertebrae using statistical models publication-title: Comput. Med. Imaging Graph. – reference: C.R. Qi, H. Su, K. Mo, L.J. Guibas, Pointnet: Deep learning on point sets for 3D classification and segmentation, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017, pp. 652–660. – year: 2022 ident: b21 article-title: Generative adversarial networks in medical image augmentation: a review publication-title: Comput. Biol. Med. – volume: 80 start-page: 463 year: 2021 end-page: 498 ident: b23 article-title: Single image 3D object reconstruction based on deep learning: A review publication-title: Multimedia Tools Appl. – volume: 2 year: 2021 ident: b10 article-title: A finite element study of the relationship between upper body weight and the loads experienced by the human lumbosacral spine, and fusion instrumentation, in a standing upright posture publication-title: Biomed. Eng. Adv. – reference: N. Wang, Y. Zhang, Z. Li, Y. Fu, W. Liu, Y.-G. Jiang, Pixel2mesh: Generating 3d mesh models from single rgb images, in: Proceedings of the European Conference on Computer Vision, ECCV, 2018, pp. 52–67. – volume: 94 start-page: 164 year: 2021 end-page: 190 ident: b14 article-title: Single-View 3D reconstruction: A Survey of deep learning methods publication-title: Comput. Graph. – volume: 29 year: 2016 ident: b37 article-title: Learning a probabilistic latent space of object shapes via 3D generative-adversarial modeling publication-title: Adv. Neural Inf. Process. Syst. – volume: 104 start-page: 915 year: 2022 end-page: 921 ident: b4 article-title: Adolescent idiopathic scoliosis: a review of aetiological theories of a multifactorial disease publication-title: Bone Joint J. – volume: 96 year: 2021 ident: b11 article-title: Effects of load mass and position on the dynamic loading of the knees, shoulders and lumbar spine during lifting: a musculoskeletal modelling approach publication-title: Applied Ergon. – volume: 96 year: 2021 ident: 10.1016/j.compbiomed.2023.106615_b11 article-title: Effects of load mass and position on the dynamic loading of the knees, shoulders and lumbar spine during lifting: a musculoskeletal modelling approach publication-title: Applied Ergon. doi: 10.1016/j.apergo.2021.103491 – start-page: 628 year: 2016 ident: 10.1016/j.compbiomed.2023.106615_b24 article-title: 3D-R2N2: A unified approach for single and multi-view 3D object reconstruction – ident: 10.1016/j.compbiomed.2023.106615_b25 doi: 10.1109/ICCV.2019.00278 – volume: 45 start-page: 2787 issue: 10 year: 2000 ident: 10.1016/j.compbiomed.2023.106615_b44 article-title: CT imaging based digitally reconstructed radiographs and their application in brachytherapy publication-title: Phys. Med. Biol. doi: 10.1088/0031-9155/45/10/305 – volume: 6 start-page: 351 issue: 3 year: 2011 ident: 10.1016/j.compbiomed.2023.106615_b16 article-title: Scaled, patient-specific 3D vertebral model reconstruction based on 2D lateral fluoroscopy publication-title: Int. J. Comput. Assist. Radiol. Surg. doi: 10.1007/s11548-010-0515-7 – volume: 140 year: 2022 ident: 10.1016/j.compbiomed.2023.106615_b36 article-title: Benchmarking automated detection of the retinal external limiting membrane in a 3D spectral domain optical coherence tomography image dataset of full thickness macular holes publication-title: Comput. Biol. Med. doi: 10.1016/j.compbiomed.2021.105070 – year: 2022 ident: 10.1016/j.compbiomed.2023.106615_b35 article-title: Utility of unsupervised deep learning using a 3D variational autoencoder in detecting inner ear abnormalities on CT images publication-title: Comput. Biol. Med. doi: 10.1016/j.compbiomed.2022.105683 – volume: 12 start-page: 269 year: 2018 ident: 10.1016/j.compbiomed.2023.106615_b20 article-title: Review of 2-D/3-D reconstruction using statistical shape and intensity models and X-ray image synthesis: Toward a unified framework publication-title: IEEE Rev. Biomed. Eng. doi: 10.1109/RBME.2018.2876450 – volume: 30 start-page: 1823 issue: 7 year: 2021 ident: 10.1016/j.compbiomed.2023.106615_b2 article-title: Adolescent idiopathic scoliosis 3D vertebral morphology, progression and nomenclature: a current concepts review publication-title: Eur. Spine J. doi: 10.1007/s00586-021-06842-z – volume: 80 start-page: 463 issue: 1 year: 2021 ident: 10.1016/j.compbiomed.2023.106615_b23 article-title: Single image 3D object reconstruction based on deep learning: A review publication-title: Multimedia Tools Appl. doi: 10.1007/s11042-020-09722-8 – ident: 10.1016/j.compbiomed.2023.106615_b39 doi: 10.1007/978-3-030-01252-6_4 – volume: 144 year: 2022 ident: 10.1016/j.compbiomed.2023.106615_b29 article-title: Adaptive soft erasure with edge self-attention for weakly supervised semantic segmentation: thyroid ultrasound image case study publication-title: Comput. Biol. Med. doi: 10.1016/j.compbiomed.2022.105347 – volume: 10 start-page: 1097 issue: 7 year: 2015 ident: 10.1016/j.compbiomed.2023.106615_b18 article-title: A statistical shape model of the human second cervical vertebra publication-title: Int. J. Comput. Assist. Radiol. Surg. doi: 10.1007/s11548-014-1121-x – volume: 2 year: 2021 ident: 10.1016/j.compbiomed.2023.106615_b10 article-title: A finite element study of the relationship between upper body weight and the loads experienced by the human lumbosacral spine, and fusion instrumentation, in a standing upright posture publication-title: Biomed. Eng. Adv. doi: 10.1016/j.bea.2021.100023 – volume: 27 start-page: 321 issue: 5 year: 2003 ident: 10.1016/j.compbiomed.2023.106615_b17 article-title: 3D/2D registration and segmentation of scoliotic vertebrae using statistical models publication-title: Comput. Med. Imaging Graph. doi: 10.1016/S0895-6111(03)00019-3 – volume: 121 year: 2020 ident: 10.1016/j.compbiomed.2023.106615_b31 article-title: 3D dense connectivity network with atrous convolutional feature pyramid for brain tumor segmentation in magnetic resonance imaging of human heads publication-title: Comput. Biol. Med. doi: 10.1016/j.compbiomed.2020.103766 – volume: 11 start-page: 359 issue: 2 year: 2021 ident: 10.1016/j.compbiomed.2023.106615_b6 article-title: Morphometric changes in the parameters of physical development of children with scoliosis publication-title: Acad. Int. Multidiscip. Res. J. – ident: 10.1016/j.compbiomed.2023.106615_b43 doi: 10.1109/CVPR.2018.00207 – year: 2022 ident: 10.1016/j.compbiomed.2023.106615_b7 article-title: Is spinal neuromuscular function asymmetrical in adolescents with idiopathic scoliosis compared to those without scoliosis?: A narrative review of surface EMG studies publication-title: J. Electromyography Kinesiol. doi: 10.1016/j.jelekin.2022.102640 – volume: 94 start-page: 164 year: 2021 ident: 10.1016/j.compbiomed.2023.106615_b14 article-title: Single-View 3D reconstruction: A Survey of deep learning methods publication-title: Comput. Graph. doi: 10.1016/j.cag.2020.12.004 – volume: 29 start-page: 607 issue: 5 year: 2021 ident: 10.1016/j.compbiomed.2023.106615_b28 article-title: Statistical shape modeling of the hip and the association with hip osteoarthritis: a systematic review publication-title: Osteoarthr. Cartil. doi: 10.1016/j.joca.2020.12.003 – volume: 3 start-page: 880 issue: 11 year: 2019 ident: 10.1016/j.compbiomed.2023.106615_b40 article-title: Patient-specific reconstruction of volumetric computed tomography images from a single projection view via deep learning publication-title: Nat. Biomed. Eng. doi: 10.1038/s41551-019-0466-4 – volume: 20 start-page: 100 year: 2020 ident: 10.1016/j.compbiomed.2023.106615_b26 article-title: Development of generic Asian pelvic bone models using CT-based 3D statistical modelling publication-title: J. Orthop. Transl. – volume: 8 year: 2020 ident: 10.1016/j.compbiomed.2023.106615_b27 article-title: Development and validation of statistical shape models of the primary functional bone segments of the foot publication-title: PeerJ doi: 10.7717/peerj.8397 – volume: 60 start-page: 1231 issue: 1 year: 2021 ident: 10.1016/j.compbiomed.2023.106615_b33 article-title: Efficient 3D deep learning model for medical image semantic segmentation publication-title: Alex. Eng. J. doi: 10.1016/j.aej.2020.10.046 – volume: 29 year: 2016 ident: 10.1016/j.compbiomed.2023.106615_b37 article-title: Learning a probabilistic latent space of object shapes via 3D generative-adversarial modeling publication-title: Adv. Neural Inf. Process. Syst. – ident: 10.1016/j.compbiomed.2023.106615_b12 doi: 10.1109/CVPR.2016.445 – ident: 10.1016/j.compbiomed.2023.106615_b38 – volume: 60 start-page: 1954 issue: 7 year: 2013 ident: 10.1016/j.compbiomed.2023.106615_b15 article-title: 3-D reconstruction of the spine from biplanar radiographs based on contour matching using the hough transform publication-title: IEEE Trans. Biomed. Eng. doi: 10.1109/TBME.2013.2246788 – year: 2021 ident: 10.1016/j.compbiomed.2023.106615_b46 – start-page: 1 year: 2022 ident: 10.1016/j.compbiomed.2023.106615_b5 article-title: Health-related quality of life in adolescents with idiopathic scoliosis: a cross-sectional study including healthy controls publication-title: Eur. Spine J. – volume: 20 start-page: 389 issue: 4 year: 2020 ident: 10.1016/j.compbiomed.2023.106615_b22 article-title: 3D reconstruction using deep learning: a survey publication-title: Commun. Inf. Syst. doi: 10.4310/CIS.2020.v20.n4.a1 – volume: 20 start-page: 5097 issue: 18 year: 2020 ident: 10.1016/j.compbiomed.2023.106615_b30 article-title: 3D deep learning on medical images: a review publication-title: Sensors doi: 10.3390/s20185097 – volume: 18 start-page: 8152 issue: 15 year: 2021 ident: 10.1016/j.compbiomed.2023.106615_b1 article-title: Incidence and surgery rate of idiopathic scoliosis: a nationwide database study publication-title: Int. J. Environ. Res. Public Health doi: 10.3390/ijerph18158152 – volume: 127 year: 2020 ident: 10.1016/j.compbiomed.2023.106615_b32 article-title: Automatic segmentation of tumors and affected organs in the abdomen using a 3D hybrid model for computed tomography imaging publication-title: Comput. Biol. Med. doi: 10.1016/j.compbiomed.2020.104097 – volume: 73 year: 2021 ident: 10.1016/j.compbiomed.2023.106615_b45 article-title: VerSe: A vertebrae labelling and segmentation benchmark for multi-detector CT images publication-title: Med. Image Anal. doi: 10.1016/j.media.2021.102166 – volume: 104 start-page: 915 issue: 8 year: 2022 ident: 10.1016/j.compbiomed.2023.106615_b4 article-title: Adolescent idiopathic scoliosis: a review of aetiological theories of a multifactorial disease publication-title: Bone Joint J. doi: 10.1302/0301-620X.104B8.BJJ-2021-1638.R1 – volume: 31 start-page: 681 issue: 6 year: 2009 ident: 10.1016/j.compbiomed.2023.106615_b19 article-title: 3D reconstruction of the spine from biplanar X-rays using parametric models based on transversal and longitudinal inferences publication-title: Med. Eng. Phys. doi: 10.1016/j.medengphy.2009.01.003 – volume: 136 year: 2021 ident: 10.1016/j.compbiomed.2023.106615_b9 article-title: Finite element analysis of the lumbar spine in adolescent idiopathic scoliosis subjected to different loads publication-title: Comput. Biol. Med. doi: 10.1016/j.compbiomed.2021.104745 – volume: 131 year: 2021 ident: 10.1016/j.compbiomed.2023.106615_b34 article-title: Automatic segmentation and location learning of neonatal cerebral ventricles in 3D ultrasound data combining CNN and CPPN publication-title: Comput. Biol. Med. doi: 10.1016/j.compbiomed.2021.104268 – ident: 10.1016/j.compbiomed.2023.106615_b42 doi: 10.1109/CVPR.2018.00745 – ident: 10.1016/j.compbiomed.2023.106615_b47 doi: 10.1109/CVPR.2019.00352 – year: 2022 ident: 10.1016/j.compbiomed.2023.106615_b3 article-title: A systematic scoping review and textual narrative synthesis of the qualitative evidence related to adolescent idiopathic scoliosis publication-title: Int. J. Orthop. Trauma Nurs. doi: 10.1016/j.ijotn.2022.100921 – ident: 10.1016/j.compbiomed.2023.106615_b41 doi: 10.1109/CVPR.2019.01087 – volume: 62 year: 2022 ident: 10.1016/j.compbiomed.2023.106615_b8 article-title: Factors influencing of quality of life in adolescent idiopathic scoliosis publication-title: Musculoskelet. Sci. Pract. – start-page: 519 year: 2006 ident: 10.1016/j.compbiomed.2023.106615_b13 article-title: A comparison and evaluation of multi-view stereo reconstruction algorithms – year: 2022 ident: 10.1016/j.compbiomed.2023.106615_b21 article-title: Generative adversarial networks in medical image augmentation: a review publication-title: Comput. Biol. Med.
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Snippet	Grasping good understanding of the weight-bearing spatial structure of the spine of a human subject in a standing position is critical for the treatment of... AbstractGrasping good understanding of the weight-bearing spatial structure of the spine of a human subject in a standing position is critical for the...
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SubjectTerms	3D reconstruction Artificial neural networks Bi-planar X-ray images Coders Data integration Decoders Decoding Deep learning Disorders Entropy Feature maps Formability Human subjects Humans Image Processing, Computer-Assisted - methods Image reconstruction Imaging, Three-Dimensional - methods Internal Medicine Neural networks Neural Networks, Computer Other Progressive decoding process Semantics Spine Spine - diagnostic imaging Vertebrae X ray imagery X-Rays
Title	BX2S-Net: Learning to reconstruct 3D spinal structures from bi-planar X-ray images
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