Dynamic graph convolutional autoencoder with node-attribute-wise attention for kidney and tumor segmentation from CT volumes
Extraction and integration of semantic connections, spatial relations and dependencies are critical in volumetric image segmentation. This is a challenging issue, especially when there are long-distance objects with close semantic relations and neighboring objects with indistinct boundaries. We prop...
Saved in:
| Published in: | Knowledge-based systems Vol. 236; p. 107360 |
|---|---|
| Main Authors: | , , , , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Amsterdam
Elsevier B.V
25.01.2022
Elsevier Science Ltd |
| Subjects: | |
| ISSN: | 0950-7051, 1872-7409 |
| Online Access: | Get full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Abstract | Extraction and integration of semantic connections, spatial relations and dependencies are critical in volumetric image segmentation. This is a challenging issue, especially when there are long-distance objects with close semantic relations and neighboring objects with indistinct boundaries. We propose a novel dynamic graph convolution (DGC) autoencoder with node-attribute-wise attention (NodeAttri-Attention) for relation inference and reasoning, with applications on kidney and tumor segmentation from computerized tomography (CT) volumes. We first introduce a new graph construction strategy for 3D volumetric image data, where graph node attributes and connections represent topological relations and high-level correlations. Then NodeAttri-Attention mechanism is proposed to obtain attention-enhanced node attributes by discriminating adaptive contributions of various features. Finally, the DGC strategy is designed to learn and integrate the complex and underlying correlations across image regions. Our DGC dynamically updates graph topology and node attributes as the graph convolutional layer gradually deepens. Experimental results and ablation studies demonstrated the effectiveness of each of our major innovations in NodeAttri-Attention DGC, especially when objects are with weak boundaries, irregular shapes, and various sizes. The improved segmentation results of embedding NodeAttri-Attention DGC to different segmentation backbones show the generality of DGC autoencoder. |
|---|---|
| AbstractList | Extraction and integration of semantic connections, spatial relations and dependencies are critical in volumetric image segmentation. This is a challenging issue, especially when there are long-distance objects with close semantic relations and neighboring objects with indistinct boundaries. We propose a novel dynamic graph convolution (DGC) autoencoder with node-attribute-wise attention (NodeAttri-Attention) for relation inference and reasoning, with applications on kidney and tumor segmentation from computerized tomography (CT) volumes. We first introduce a new graph construction strategy for 3D volumetric image data, where graph node attributes and connections represent topological relations and high-level correlations. Then NodeAttri-Attention mechanism is proposed to obtain attention-enhanced node attributes by discriminating adaptive contributions of various features. Finally, the DGC strategy is designed to learn and integrate the complex and underlying correlations across image regions. Our DGC dynamically updates graph topology and node attributes as the graph convolutional layer gradually deepens. Experimental results and ablation studies demonstrated the effectiveness of each of our major innovations in NodeAttri-Attention DGC, especially when objects are with weak boundaries, irregular shapes, and various sizes. The improved segmentation results of embedding NodeAttri-Attention DGC to different segmentation backbones show the generality of DGC autoencoder. |
| ArticleNumber | 107360 |
| Author | Cui, Hui Xuan, Ping Wang, Linlin Nakaguchi, Toshiya Zhang, Tiangang Duh, Henry B.L. Zhang, Hongda |
| Author_xml | – sequence: 1 givenname: Ping orcidid: 0000-0001-5328-691X surname: Xuan fullname: Xuan, Ping organization: School of Computer Science and Technology, Heilongjiang University, Harbin, China – sequence: 2 givenname: Hui surname: Cui fullname: Cui, Hui organization: Department of Computer Science and Information Technology, La Trobe University, Melbourne, Australia – sequence: 3 givenname: Hongda surname: Zhang fullname: Zhang, Hongda email: hongda.zhang.hlju@gmail.com organization: School of Computer Science and Technology, Heilongjiang University, Harbin, China – sequence: 4 givenname: Tiangang surname: Zhang fullname: Zhang, Tiangang email: zhang@hlju.edu.cn organization: School of Mathematical Science, Heilongjiang University, Harbin, China – sequence: 5 givenname: Linlin surname: Wang fullname: Wang, Linlin organization: Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China – sequence: 6 givenname: Toshiya surname: Nakaguchi fullname: Nakaguchi, Toshiya organization: Center for Frontier Medical Engineering, Chiba University, Chiba, Japan – sequence: 7 givenname: Henry B.L. orcidid: 0000-0003-4808-6109 surname: Duh fullname: Duh, Henry B.L. organization: Department of Computer Science and Information Technology, La Trobe University, Melbourne, Australia |
| BookMark | eNqFkE1PAyEQhonRxPrxDzyQeN4Ku9uF9WBi6mdi4kXPhIWhpXahAqtp4o-XZj150BPDzPvO5H2O0L7zDhA6o2RKCW0uVtM35-M2TktS0txiVUP20IRyVhasJu0-mpB2RgpGZvQQHcW4IoSUJeUT9HWzdbK3Ci-C3Cyx8u7Dr4dkvZNrLIfkwSmvIeBPm5bY5bKQKQXbDQmKTxsB5y-4nQEbH_Cb1Q62WDqN09DnRoRFn-dyVATf4_kL3t3oIZ6gAyPXEU5_3mP0enf7Mn8onp7vH-fXT4WqqjoVLaO05o2WHVUN6zgzEkA2XdcxU3PeUq21kdzMtKmMyvOWGkq44ZIorSpaHaPzce8m-PcBYhIrP4ScMIqyqWjDeN3UWVWPKhV8jAGM2ATby7AVlIgdZ7ESI2ex4yxGztl2-cum7Bg3BWnX_5mvRjPk-B8WgojKZuSgbQCVhPb27wXfWhWiQw |
| CitedBy_id | crossref_primary_10_1016_j_knosys_2022_109438 crossref_primary_10_1088_1361_6560_ad294c crossref_primary_10_1016_j_compbiomed_2024_108640 crossref_primary_10_1007_s10278_023_00900_2 crossref_primary_10_1016_j_neunet_2025_108042 crossref_primary_10_1109_ACCESS_2024_3410833 crossref_primary_10_1109_JBHI_2022_3214999 crossref_primary_10_1007_s00530_022_01020_7 crossref_primary_10_1109_ACCESS_2025_3577393 crossref_primary_10_1155_2023_8342104 crossref_primary_10_1016_j_bspc_2024_106049 crossref_primary_10_1109_TIP_2024_3451936 crossref_primary_10_1140_epjp_s13360_025_06686_2 crossref_primary_10_1016_j_asoc_2024_112069 crossref_primary_10_3390_cancers15123189 crossref_primary_10_3390_electronics13163226 crossref_primary_10_1007_s11063_022_11034_x crossref_primary_10_1016_j_bspc_2023_105614 crossref_primary_10_1088_1361_6560_ac9e3f crossref_primary_10_1007_s11042_023_15882_0 |
| Cites_doi | 10.1016/j.imu.2020.100357 10.1109/TPAMI.2017.2712691 10.1073/pnas.1715832114 10.1016/j.cmpb.2018.03.018 10.1007/978-3-030-01240-3_17 10.1109/CVPR.2018.00813 10.1109/ICCV.2017.89 10.1007/978-3-030-01228-1_25 10.1016/j.media.2017.07.005 10.1109/CVPR.2016.90 10.1109/ISBI.2014.6868101 10.1109/34.232073 10.1007/s13735-017-0141-z 10.1109/CVPR.2017.650 10.1109/CVPR42600.2020.00897 10.3390/cells8091012 10.5244/C.27.32 10.1016/S1076-6332(03)00671-8 10.1109/CVPR.2019.00052 10.1109/CVPR.2017.660 10.1109/CVPR.2018.00745 10.1109/CVPR.2019.00326 |
| ContentType | Journal Article |
| Copyright | 2021 Copyright Elsevier Science Ltd. Jan 25, 2022 |
| Copyright_xml | – notice: 2021 – notice: Copyright Elsevier Science Ltd. Jan 25, 2022 |
| DBID | AAYXX CITATION 7SC 8FD E3H F2A JQ2 L7M L~C L~D |
| DOI | 10.1016/j.knosys.2021.107360 |
| DatabaseName | CrossRef Computer and Information Systems Abstracts Technology Research Database Library & Information Sciences Abstracts (LISA) Library & Information Science Abstracts (LISA) ProQuest Computer Science Collection Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional |
| DatabaseTitle | CrossRef Technology Research Database Computer and Information Systems Abstracts – Academic Library and Information Science Abstracts (LISA) ProQuest Computer Science Collection Computer and Information Systems Abstracts Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Professional |
| DatabaseTitleList | Technology Research Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Computer Science |
| EISSN | 1872-7409 |
| ExternalDocumentID | 10_1016_j_knosys_2021_107360 S0950705121006225 |
| GroupedDBID | --K --M .DC .~1 0R~ 1B1 1~. 1~5 4.4 457 4G. 5VS 7-5 71M 77K 8P~ 9JN AACTN AAEDT AAEDW AAIAV AAIKJ AAKOC AALRI AAOAW AAQFI AAXUO AAYFN ABAOU ABBOA ABIVO ABJNI ABMAC ABYKQ ACAZW ACDAQ ACGFS ACRLP ACZNC ADBBV ADEZE ADGUI ADTZH AEBSH AECPX AEKER AENEX AFKWA AFTJW AGHFR AGUBO AGYEJ AHHHB AHJVU AHZHX AIALX AIEXJ AIKHN AITUG AJOXV ALMA_UNASSIGNED_HOLDINGS AMFUW AMRAJ AOUOD ARUGR AXJTR BJAXD BKOJK BLXMC CS3 DU5 EBS EFJIC EFLBG EO8 EO9 EP2 EP3 FDB FIRID FNPLU FYGXN G-Q GBLVA GBOLZ IHE J1W JJJVA KOM LG9 LY7 M41 MHUIS MO0 N9A O-L O9- OAUVE OZT P-8 P-9 P2P PC. PQQKQ Q38 ROL RPZ SDF SDG SDP SES SPC SPCBC SST SSV SSW SSZ T5K WH7 XPP ZMT ~02 ~G- 29L 77I 9DU AAQXK AATTM AAXKI AAYWO AAYXX ABDPE ABWVN ABXDB ACLOT ACNNM ACRPL ACVFH ADCNI ADJOM ADMUD ADNMO AEIPS AEUPX AFJKZ AFPUW AGQPQ AIGII AIIUN AKBMS AKRWK AKYEP ANKPU APXCP ASPBG AVWKF AZFZN CITATION EFKBS EJD FEDTE FGOYB G-2 HLZ HVGLF HZ~ R2- SBC SET SEW UHS WUQ ~HD 7SC 8FD E3H F2A JQ2 L7M L~C L~D |
| ID | FETCH-LOGICAL-c334t-9711486dab1c67b87faeea6bbb7f48891dddfa8f5df3fcb8791f108f8a0cdc313 |
| ISICitedReferencesCount | 25 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000788654600008&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 0950-7051 |
| IngestDate | Fri Nov 14 18:46:04 EST 2025 Sat Nov 29 07:11:40 EST 2025 Tue Nov 18 21:00:20 EST 2025 Fri Feb 23 02:41:40 EST 2024 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | Node-attribute-wise attention Kidney and tumor segmentation Long-distance relationship between nodes Graph node attributes Dynamic graph convolutional autoencoder |
| Language | English |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-c334t-9711486dab1c67b87faeea6bbb7f48891dddfa8f5df3fcb8791f108f8a0cdc313 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| ORCID | 0000-0001-5328-691X 0000-0003-4808-6109 |
| PQID | 2631678464 |
| PQPubID | 2035257 |
| ParticipantIDs | proquest_journals_2631678464 crossref_primary_10_1016_j_knosys_2021_107360 crossref_citationtrail_10_1016_j_knosys_2021_107360 elsevier_sciencedirect_doi_10_1016_j_knosys_2021_107360 |
| PublicationCentury | 2000 |
| PublicationDate | 2022-01-25 |
| PublicationDateYYYYMMDD | 2022-01-25 |
| PublicationDate_xml | – month: 01 year: 2022 text: 2022-01-25 day: 25 |
| PublicationDecade | 2020 |
| PublicationPlace | Amsterdam |
| PublicationPlace_xml | – name: Amsterdam |
| PublicationTitle | Knowledge-based systems |
| PublicationYear | 2022 |
| Publisher | Elsevier B.V Elsevier Science Ltd |
| Publisher_xml | – name: Elsevier B.V – name: Elsevier Science Ltd |
| References | X. Wang, R. Girshick, A. Gupta, K. He, Non-local Neural Networks, in: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018, pp. 7794–7803. Cui, Wang, Zhou, Gong, Eberl, Yin, Wang, Feng, Fulham (b34) 2018; 159 G. Csurka, D. Larlus, F. Perronnin, F. Meylan, What is a good evaluation measure for semantic segmentation?, in: BMVC, Vol. 27, p. 2013. Xuan, Pan, Zhang, Liu, Sun (b28) 2019; 8 Litjens, Kooi, Bejnordi, Setio, Ciompi, Ghafoorian, Van Der Laak, Van Ginneken, Sánchez (b1) 2017; 42 Jin, Cui, Sun, Meng, Su (b4) 2020 J. Fu, J. Liu, H. Tian, Y. Li, Y. Bao, Z. Fang, H. Lu, Dual attention network for scene segmentation, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2019, pp. 3146–3154. Zou, Warfield, Bharatha, Tempany, Kaus, Haker, Wells III, Jolesz, Kikinis (b40) 2004; 11 J. Dai, H. Qi, Y. Xiong, Y. Li, G. Zhang, H. Hu, Y. Wei, Deformable convolutional networks, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017, pp. 764–773. Mo, Cai, Lin, Tong, Chen, Wang, Hu, Iwamoto, Han, Chen (b22) 2020 Cuingnet, Prevost, Lesage, Cohen, Mory, Ardon (b2) 2012 Zhao, Jiang, Peña Queralta, Westerlund (b8) 2020; 19 H. Zhao, Y. Zhang, S. Liu, J. Shi, C. Change Loy, D. Lin, J. Jia, Psanet: Point-wise spatial attention network for scene parsing, in: Proceedings of the European Conference on Computer Vision (ECCV), 2018, pp. 267–283. 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. Shuai, Zuo, Wang, Wang (b18) 2017; 40 Isensee, Jaeger, Kohl, Petersen, Maier-Hein (b36) 2020 Yang, Li, Pan, Kong, Wu, Shu, Luo, Dillenseger, Coatrieux, Tang (b35) 2018 Fan, Chu, Latecki, Ling (b17) 2019 Z. Ying, J. You, C. Morris, X. Ren, W. Hamilton, J. Leskovec, Hierarchical graph representation learning with differentiable pooling, in: Advances in Neural Information Processing Systems, 2018, pp. 4800–4810. Xuan, Gao, Sheng, Zhang, Nakaguchi (b29) 2020 Kipf, Welling (b26) 2016 H. Cui, X. Wang, J. Zhou, M. Fulham, S. Eberl, D. Feng, Topology constraint graph-based model for non-small-cell lung tumor segmentation from PET volumes, in: 2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI), 2014, pp. 1243–1246. K. He, X. Zhang, S. Ren, J. Sun, Deep residual learning for image recognition, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 770–778. Wu, Pan, Chen, Long, Zhang, Philip (b27) 2020 Huttenlocher, Klanderman, Rucklidge (b42) 1993; 15 Chen, Dou, Jin, Chen, Qin, Heng (b38) 2019 H. Zhao, J. Shi, X. Qi, X. Wang, J. Jia, Pyramid scene parsing network, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017, pp. 2881–2890. Çiçek, Abdulkadir, Lienkamp, Brox, Ronneberger (b43) 2016 Pelt, Sethian (b7) 2018; 115 G. Bertasius, L. Torresani, S.X. Yu, J. Shi, Convolutional random walk networks for semantic image segmentation, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017, pp. 858–866. X. Wang, A. Gupta, Videos as space-time region graphs, in: Proceedings of the European Conference on Computer Vision (ECCV), 2018, pp. 399–417. X. Liang, Z. Hu, H. Zhang, L. Lin, E.P. Xing, Symbolic graph reasoning meets convolutions, in: Advances in Neural Information Processing Systems, 2018, pp. 1853–1863. Guo, Liu, Georgiou, Lew (b6) 2018; 7 Kipf, Welling (b23) 2016 Y. Chen, M. Rohrbach, Z. Yan, Y. Shuicheng, J. Feng, Y. Kalantidis, Graph-based global reasoning networks, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2019, pp. 433–442. Heller, Sathianathen, Kalapara, Walczak, Moore, Kaluzniak, Rosenberg, Blake, Rengel, Oestreich (b39) 2019 Y. Li, A. Gupta, Beyond grids: Learning graph representations for visual recognition, in: Advances in Neural Information Processing Systems, 2018, pp. 9225–9235. Isensee, Petersen, Klein, Zimmerer, Jaeger, Kohl, Wasserthal, Koehler, Norajitra, Wirkert (b37) 2018 Heller, Isensee, Maier-Hein, Hou, Xie, Li, Nan, Mu, Lin, Han (b3) 2019; 67 Chen, Kalantidis, Li, Yan, Feng (b15) 2018 Cui, Xu, Li, Wang, Duh (b5) 2020 Li, Pan, Wu, Wen, Qin (b9) 2020 X. Li, Y. Yang, Q. Zhao, T. Shen, Z. Lin, H. Liu, Spatial Pyramid Based Graph Reasoning for Semantic Segmentation, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2020, pp. 8950–8959. Çiçek (10.1016/j.knosys.2021.107360_b43) 2016 Xuan (10.1016/j.knosys.2021.107360_b29) 2020 Yang (10.1016/j.knosys.2021.107360_b35) 2018 10.1016/j.knosys.2021.107360_b21 10.1016/j.knosys.2021.107360_b20 10.1016/j.knosys.2021.107360_b41 Cui (10.1016/j.knosys.2021.107360_b5) 2020 Jin (10.1016/j.knosys.2021.107360_b4) 2020 10.1016/j.knosys.2021.107360_b25 10.1016/j.knosys.2021.107360_b24 10.1016/j.knosys.2021.107360_b44 Chen (10.1016/j.knosys.2021.107360_b38) 2019 10.1016/j.knosys.2021.107360_b16 Kipf (10.1016/j.knosys.2021.107360_b23) 2016 Cui (10.1016/j.knosys.2021.107360_b34) 2018; 159 10.1016/j.knosys.2021.107360_b19 Zou (10.1016/j.knosys.2021.107360_b40) 2004; 11 Isensee (10.1016/j.knosys.2021.107360_b37) 2018 Chen (10.1016/j.knosys.2021.107360_b15) 2018 Pelt (10.1016/j.knosys.2021.107360_b7) 2018; 115 Xuan (10.1016/j.knosys.2021.107360_b28) 2019; 8 Zhao (10.1016/j.knosys.2021.107360_b8) 2020; 19 Kipf (10.1016/j.knosys.2021.107360_b26) 2016 Huttenlocher (10.1016/j.knosys.2021.107360_b42) 1993; 15 10.1016/j.knosys.2021.107360_b10 10.1016/j.knosys.2021.107360_b32 Isensee (10.1016/j.knosys.2021.107360_b36) 2020 10.1016/j.knosys.2021.107360_b31 10.1016/j.knosys.2021.107360_b30 Litjens (10.1016/j.knosys.2021.107360_b1) 2017; 42 Heller (10.1016/j.knosys.2021.107360_b39) 2019 Guo (10.1016/j.knosys.2021.107360_b6) 2018; 7 10.1016/j.knosys.2021.107360_b14 Fan (10.1016/j.knosys.2021.107360_b17) 2019 10.1016/j.knosys.2021.107360_b13 10.1016/j.knosys.2021.107360_b12 10.1016/j.knosys.2021.107360_b11 10.1016/j.knosys.2021.107360_b33 Li (10.1016/j.knosys.2021.107360_b9) 2020 Mo (10.1016/j.knosys.2021.107360_b22) 2020 Heller (10.1016/j.knosys.2021.107360_b3) 2019; 67 Cuingnet (10.1016/j.knosys.2021.107360_b2) 2012 Wu (10.1016/j.knosys.2021.107360_b27) 2020 Shuai (10.1016/j.knosys.2021.107360_b18) 2017; 40 |
| References_xml | – reference: X. Li, Y. Yang, Q. Zhao, T. Shen, Z. Lin, H. Liu, Spatial Pyramid Based Graph Reasoning for Semantic Segmentation, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2020, pp. 8950–8959. – start-page: 424 year: 2016 end-page: 432 ident: b43 article-title: 3D U-net: learning dense volumetric segmentation from sparse annotation publication-title: International Conference on Medical Image Computing and Computer-Assisted Intervention – year: 2016 ident: b23 article-title: Semi-supervised classification with graph convolutional networks – volume: 67 year: 2019 ident: b3 article-title: The state of the art in kidney and kidney tumor segmentation in contrast-enhanced ct imaging: Results of the kits19 challenge publication-title: Med. Image Anal. – reference: X. Wang, A. Gupta, Videos as space-time region graphs, in: Proceedings of the European Conference on Computer Vision (ECCV), 2018, pp. 399–417. – start-page: 197 year: 2020 end-page: 206 ident: b9 article-title: Memory-efficient automatic kidney and tumor segmentation based on non-local context guided 3D U-Net publication-title: International Conference on Medical Image Computing and Computer-Assisted Intervention – year: 2018 ident: b15 article-title: A – reference: K. He, X. Zhang, S. Ren, J. Sun, Deep residual learning for image recognition, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 770–778. – reference: X. Wang, R. Girshick, A. Gupta, K. He, Non-local Neural Networks, in: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018, pp. 7794–7803. – reference: H. Zhao, J. Shi, X. Qi, X. Wang, J. Jia, Pyramid scene parsing network, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017, pp. 2881–2890. – start-page: 3790 year: 2018 end-page: 3795 ident: b35 article-title: Automatic segmentation of kidney and renal tumor in ct images based on 3d fully convolutional neural network with pyramid pooling module publication-title: 2018 24th International Conference on Pattern Recognition (ICPR) – start-page: 447 year: 2019 end-page: 456 ident: b38 article-title: Robust multimodal brain tumor segmentation via feature disentanglement and gated fusion publication-title: International Conference on Medical Image Computing and Computer-Assisted Intervention – start-page: 66 year: 2012 end-page: 74 ident: b2 article-title: Automatic detection and segmentation of kidneys in 3D CT images using random forests publication-title: International Conference on Medical Image Computing and Computer-Assisted Intervention – reference: H. Cui, X. Wang, J. Zhou, M. Fulham, S. Eberl, D. Feng, Topology constraint graph-based model for non-small-cell lung tumor segmentation from PET volumes, in: 2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI), 2014, pp. 1243–1246. – year: 2019 ident: b39 article-title: The kits19 challenge data: 300 kidney tumor cases with clinical context, ct semantic segmentations, and surgical outcomes – volume: 159 start-page: 211 year: 2018 end-page: 222 ident: b34 article-title: A topo-graph model for indistinct target boundary definition from anatomical images publication-title: Comput. Methods Programs Biomed. – reference: Z. Ying, J. You, C. Morris, X. Ren, W. Hamilton, J. Leskovec, Hierarchical graph representation learning with differentiable pooling, in: Advances in Neural Information Processing Systems, 2018, pp. 4800–4810. – volume: 42 start-page: 60 year: 2017 end-page: 88 ident: b1 article-title: A survey on deep learning in medical image analysis publication-title: Med. Image Anal. – reference: G. Bertasius, L. Torresani, S.X. Yu, J. Shi, Convolutional random walk networks for semantic image segmentation, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017, pp. 858–866. – volume: 40 start-page: 1480 year: 2017 end-page: 1493 ident: b18 article-title: Scene segmentation with dag-recurrent neural networks publication-title: IEEE Trans. Pattern Anal. Mach. Intell. – 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. – year: 2020 ident: b27 article-title: A comprehensive survey on graph neural networks publication-title: IEEE Trans. Neural Netw. Learn. Syst. – volume: 15 start-page: 850 year: 1993 end-page: 863 ident: b42 article-title: Comparing images using the Hausdorff distance publication-title: IEEE Trans. Pattern Anal. Mach. Intell. – volume: 115 start-page: 254 year: 2018 end-page: 259 ident: b7 article-title: A mixed-scale dense convolutional neural network for image analysis publication-title: Proc. Natl. Acad. Sci. – start-page: 429 year: 2020 end-page: 438 ident: b22 article-title: Multimodal priors guided segmentation of liver lesions in MRI using mutual information based graph co-attention networks publication-title: International Conference on Medical Image Computing and Computer-Assisted Intervention – year: 2020 ident: b29 article-title: Graph convolutional autoencoder and fully-connected autoencoder with attention mechanism based method for predicting drug-disease associations publication-title: IEEE J. Biomed. Health Inform. – start-page: 212 year: 2020 end-page: 220 ident: b5 article-title: Collaborative learning of cross-channel clinical attention for radiotherapy-related esophageal fistula prediction from CT publication-title: International Conference on Medical Image Computing and Computer-Assisted Intervention – reference: H. Zhao, Y. Zhang, S. Liu, J. Shi, C. Change Loy, D. Lin, J. Jia, Psanet: Point-wise spatial attention network for scene parsing, in: Proceedings of the European Conference on Computer Vision (ECCV), 2018, pp. 267–283. – reference: J. Dai, H. Qi, Y. Xiong, Y. Li, G. Zhang, H. Hu, Y. Wei, Deformable convolutional networks, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017, pp. 764–773. – start-page: 1 year: 2020 end-page: 9 ident: b36 article-title: NnU-Net: a self-configuring method for deep learning-based biomedical image segmentation publication-title: Nature Methods – reference: Y. Li, A. Gupta, Beyond grids: Learning graph representations for visual recognition, in: Advances in Neural Information Processing Systems, 2018, pp. 9225–9235. – volume: 7 start-page: 87 year: 2018 end-page: 93 ident: b6 article-title: A review of semantic segmentation using deep neural networks publication-title: Int. J. Multimed. Inf. Retr. – year: 2016 ident: b26 article-title: Semi-supervised classification with graph convolutional networks – start-page: 1816 year: 2019 end-page: 1825 ident: b17 article-title: Scene parsing via dense recurrent neural networks with attentional selection publication-title: 2019 IEEE Winter Conference on Applications of Computer Vision (WACV) – reference: X. Liang, Z. Hu, H. Zhang, L. Lin, E.P. Xing, Symbolic graph reasoning meets convolutions, in: Advances in Neural Information Processing Systems, 2018, pp. 1853–1863. – volume: 8 start-page: 1012 year: 2019 ident: b28 article-title: Graph convolutional network and convolutional neural network based method for predicting lncRNA-disease associations publication-title: Cells – reference: G. Csurka, D. Larlus, F. Perronnin, F. Meylan, What is a good evaluation measure for semantic segmentation?, in: BMVC, Vol. 27, p. 2013. – reference: Y. Chen, M. Rohrbach, Z. Yan, Y. Shuicheng, J. Feng, Y. Kalantidis, Graph-based global reasoning networks, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2019, pp. 433–442. – reference: J. Fu, J. Liu, H. Tian, Y. Li, Y. Bao, Z. Fang, H. Lu, Dual attention network for scene segmentation, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2019, pp. 3146–3154. – year: 2020 ident: b4 article-title: Cascade knowledge diffusion network for skin lesion diagnosis and segmentation publication-title: Appl. Soft Comput. – volume: 11 start-page: 178 year: 2004 end-page: 189 ident: b40 article-title: Statistical validation of image segmentation quality based on a spatial overlap index1: scientific reports publication-title: Academic Radiol. – year: 2018 ident: b37 article-title: NnU-Net: Self-adapting framework for U-net-based medical image segmentation – volume: 19 year: 2020 ident: b8 article-title: MSS U-Net: 3D segmentation of kidneys and tumors from CT images with a multi-scale supervised U-Net publication-title: Inform. Med. Unlocked – volume: 19 year: 2020 ident: 10.1016/j.knosys.2021.107360_b8 article-title: MSS U-Net: 3D segmentation of kidneys and tumors from CT images with a multi-scale supervised U-Net publication-title: Inform. Med. Unlocked doi: 10.1016/j.imu.2020.100357 – volume: 40 start-page: 1480 issue: 6 year: 2017 ident: 10.1016/j.knosys.2021.107360_b18 article-title: Scene segmentation with dag-recurrent neural networks publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/TPAMI.2017.2712691 – volume: 67 year: 2019 ident: 10.1016/j.knosys.2021.107360_b3 article-title: The state of the art in kidney and kidney tumor segmentation in contrast-enhanced ct imaging: Results of the kits19 challenge publication-title: Med. Image Anal. – volume: 115 start-page: 254 issue: 2 year: 2018 ident: 10.1016/j.knosys.2021.107360_b7 article-title: A mixed-scale dense convolutional neural network for image analysis publication-title: Proc. Natl. Acad. Sci. doi: 10.1073/pnas.1715832114 – volume: 159 start-page: 211 year: 2018 ident: 10.1016/j.knosys.2021.107360_b34 article-title: A topo-graph model for indistinct target boundary definition from anatomical images publication-title: Comput. Methods Programs Biomed. doi: 10.1016/j.cmpb.2018.03.018 – ident: 10.1016/j.knosys.2021.107360_b11 doi: 10.1007/978-3-030-01240-3_17 – ident: 10.1016/j.knosys.2021.107360_b14 doi: 10.1109/CVPR.2018.00813 – start-page: 1816 year: 2019 ident: 10.1016/j.knosys.2021.107360_b17 article-title: Scene parsing via dense recurrent neural networks with attentional selection – ident: 10.1016/j.knosys.2021.107360_b12 doi: 10.1109/ICCV.2017.89 – ident: 10.1016/j.knosys.2021.107360_b13 doi: 10.1007/978-3-030-01228-1_25 – ident: 10.1016/j.knosys.2021.107360_b19 – start-page: 447 year: 2019 ident: 10.1016/j.knosys.2021.107360_b38 article-title: Robust multimodal brain tumor segmentation via feature disentanglement and gated fusion – volume: 42 start-page: 60 year: 2017 ident: 10.1016/j.knosys.2021.107360_b1 article-title: A survey on deep learning in medical image analysis publication-title: Med. Image Anal. doi: 10.1016/j.media.2017.07.005 – ident: 10.1016/j.knosys.2021.107360_b44 doi: 10.1109/CVPR.2016.90 – ident: 10.1016/j.knosys.2021.107360_b33 doi: 10.1109/ISBI.2014.6868101 – start-page: 212 year: 2020 ident: 10.1016/j.knosys.2021.107360_b5 article-title: Collaborative learning of cross-channel clinical attention for radiotherapy-related esophageal fistula prediction from CT – year: 2018 ident: 10.1016/j.knosys.2021.107360_b37 – start-page: 424 year: 2016 ident: 10.1016/j.knosys.2021.107360_b43 article-title: 3D U-net: learning dense volumetric segmentation from sparse annotation – volume: 15 start-page: 850 issue: 9 year: 1993 ident: 10.1016/j.knosys.2021.107360_b42 article-title: Comparing images using the Hausdorff distance publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/34.232073 – volume: 7 start-page: 87 issue: 2 year: 2018 ident: 10.1016/j.knosys.2021.107360_b6 article-title: A review of semantic segmentation using deep neural networks publication-title: Int. J. Multimed. Inf. Retr. doi: 10.1007/s13735-017-0141-z – year: 2018 ident: 10.1016/j.knosys.2021.107360_b15 – start-page: 3790 year: 2018 ident: 10.1016/j.knosys.2021.107360_b35 article-title: Automatic segmentation of kidney and renal tumor in ct images based on 3d fully convolutional neural network with pyramid pooling module – ident: 10.1016/j.knosys.2021.107360_b31 doi: 10.1109/CVPR.2017.650 – ident: 10.1016/j.knosys.2021.107360_b24 doi: 10.1109/CVPR42600.2020.00897 – volume: 8 start-page: 1012 issue: 9 year: 2019 ident: 10.1016/j.knosys.2021.107360_b28 article-title: Graph convolutional network and convolutional neural network based method for predicting lncRNA-disease associations publication-title: Cells doi: 10.3390/cells8091012 – year: 2016 ident: 10.1016/j.knosys.2021.107360_b26 – ident: 10.1016/j.knosys.2021.107360_b30 – ident: 10.1016/j.knosys.2021.107360_b41 doi: 10.5244/C.27.32 – year: 2020 ident: 10.1016/j.knosys.2021.107360_b4 article-title: Cascade knowledge diffusion network for skin lesion diagnosis and segmentation publication-title: Appl. Soft Comput. – volume: 11 start-page: 178 issue: 2 year: 2004 ident: 10.1016/j.knosys.2021.107360_b40 article-title: Statistical validation of image segmentation quality based on a spatial overlap index1: scientific reports publication-title: Academic Radiol. doi: 10.1016/S1076-6332(03)00671-8 – ident: 10.1016/j.knosys.2021.107360_b21 doi: 10.1109/CVPR.2019.00052 – start-page: 429 year: 2020 ident: 10.1016/j.knosys.2021.107360_b22 article-title: Multimodal priors guided segmentation of liver lesions in MRI using mutual information based graph co-attention networks – ident: 10.1016/j.knosys.2021.107360_b32 doi: 10.1109/CVPR.2017.650 – start-page: 197 year: 2020 ident: 10.1016/j.knosys.2021.107360_b9 article-title: Memory-efficient automatic kidney and tumor segmentation based on non-local context guided 3D U-Net – ident: 10.1016/j.knosys.2021.107360_b10 doi: 10.1109/CVPR.2017.660 – year: 2019 ident: 10.1016/j.knosys.2021.107360_b39 – year: 2020 ident: 10.1016/j.knosys.2021.107360_b27 article-title: A comprehensive survey on graph neural networks publication-title: IEEE Trans. Neural Netw. Learn. Syst. – start-page: 66 year: 2012 ident: 10.1016/j.knosys.2021.107360_b2 article-title: Automatic detection and segmentation of kidneys in 3D CT images using random forests – ident: 10.1016/j.knosys.2021.107360_b25 doi: 10.1109/CVPR.2018.00745 – ident: 10.1016/j.knosys.2021.107360_b16 doi: 10.1109/CVPR.2019.00326 – ident: 10.1016/j.knosys.2021.107360_b20 – start-page: 1 year: 2020 ident: 10.1016/j.knosys.2021.107360_b36 article-title: NnU-Net: a self-configuring method for deep learning-based biomedical image segmentation publication-title: Nature Methods – year: 2016 ident: 10.1016/j.knosys.2021.107360_b23 – year: 2020 ident: 10.1016/j.knosys.2021.107360_b29 article-title: Graph convolutional autoencoder and fully-connected autoencoder with attention mechanism based method for predicting drug-disease associations publication-title: IEEE J. Biomed. Health Inform. |
| SSID | ssj0002218 |
| Score | 2.456192 |
| Snippet | Extraction and integration of semantic connections, spatial relations and dependencies are critical in volumetric image segmentation. This is a challenging... |
| SourceID | proquest crossref elsevier |
| SourceType | Aggregation Database Enrichment Source Index Database Publisher |
| StartPage | 107360 |
| SubjectTerms | Ablation Attention Attributes Boundaries Computed tomography Computerization Dynamic graph convolutional autoencoder Extraction Graph node attributes Image segmentation Inference Innovations Kidney and tumor segmentation Kidneys Long-distance relationship between nodes Medical imaging Node-attribute-wise attention Nodes Segmentation Semantic relations Semantics Tomography Topology Tumors |
| Title | Dynamic graph convolutional autoencoder with node-attribute-wise attention for kidney and tumor segmentation from CT volumes |
| URI | https://dx.doi.org/10.1016/j.knosys.2021.107360 https://www.proquest.com/docview/2631678464 |
| Volume | 236 |
| WOSCitedRecordID | wos000788654600008&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVESC databaseName: ScienceDirect Freedom Collection customDbUrl: eissn: 1872-7409 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0002218 issn: 0950-7051 databaseCode: AIEXJ dateStart: 19950201 isFulltext: true titleUrlDefault: https://www.sciencedirect.com providerName: Elsevier |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1Lb9QwELaWlgMX3ohCQT4gLitXeThxcqyWrQpUSw-ptLcoD3u1fXiXbtL20J_Ej2T8yqa0UDhwiSJ742Qzn8fjycw3CH1QGWiiECHMtKImlFFPzbmSVCL1eCRKUfNCF5tgk0kynaaHg8EPlwtzccqkTK6u0uV_FTW0gbBV6uw_iLsbFBrgHIQORxA7HP9K8J9MjfmhpqLWUeX2dooVoG0WirlSEUhoD6yEU1I0puwVJ5fzFR8qxk3ZhSCezGvJDUlT055Bw4rPzmzCkjTZKaNsaJTcqm_qfnXeOqJWytpyRncm_LQ1rtdDt3bqOG1TQ7ud3_Jn7y_kbO086JozQPessCNY50WgokCISXQ2HjWXVeMU2TqUybgoPcI8S0jLjYJOGOwIqJf2NXgQ9nWwf-fKYJwUxzsncgH_dwcexodGFppqBjeJuCff8r2jg4M8G0-zj8vvRNUoU9_ybcGWB2gzYFEKOnRz9_N4-qVb-YNA-5O753apmjqe8PaNf2cK_WIUaEsne4oe2y0K3jXQeoYGXD5HT1z5D2xf4gt0bZGGNdLwDaThHtKwQhq-A2m4QxoGpGGDNAxIwxppuI80rJCGRxm2SHuJjvbG2Wif2GIepApD2pCUqZ13XBelX8WsTJgoOC_isiyZgEUk9eu6FkUiolqEooL-1Be-l4ik8Kq6Cv3wFdqQC8lfIwxbLKpJnKKwopSWMDQrvZQHgsYVjaMtFLr3mleW6V4VXDnNXUjjcW6kkStp5EYaW4h0Vy0N08s9v2dOZLm1Vo0VmgPk7rly20k4t4oD-mPFSQG7Afrmz91v0aP1TNpGG815y9-hh9VFM1-dv7eQ_AlW78eJ |
| linkProvider | Elsevier |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Dynamic+graph+convolutional+autoencoder+with+node-attribute-wise+attention+for+kidney+and+tumor+segmentation+from+CT+volumes&rft.jtitle=Knowledge-based+systems&rft.au=Xuan%2C+Ping&rft.au=Cui%2C+Hui&rft.au=Zhang%2C+Hongda&rft.au=Zhang%2C+Tiangang&rft.date=2022-01-25&rft.pub=Elsevier+Science+Ltd&rft.issn=0950-7051&rft.eissn=1872-7409&rft.volume=236&rft.spage=1&rft_id=info:doi/10.1016%2Fj.knosys.2021.107360&rft.externalDBID=NO_FULL_TEXT |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0950-7051&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0950-7051&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0950-7051&client=summon |