{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T16:56:47Z","timestamp":1777568207322,"version":"3.51.4"},"publisher-location":"Cham","reference-count":61,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031734038","type":"print"},{"value":"9783031734045","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,10,30]],"date-time":"2024-10-30T00:00:00Z","timestamp":1730246400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,10,30]],"date-time":"2024-10-30T00:00:00Z","timestamp":1730246400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025]]},"DOI":"10.1007\/978-3-031-73404-5_9","type":"book-chapter","created":{"date-parts":[[2024,10,29]],"date-time":"2024-10-29T16:03:13Z","timestamp":1730217793000},"page":"143-160","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Representing Topological Self-similarity Using Fractal Feature Maps for\u00a0Accurate Segmentation of\u00a0Tubular Structures"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0222-7925","authenticated-orcid":false,"given":"Jiaxing","family":"Huang","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5988-5331","authenticated-orcid":false,"given":"Yanfeng","family":"Zhou","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6547-1634","authenticated-orcid":false,"given":"Yaoru","family":"Luo","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1006-6383","authenticated-orcid":false,"given":"Guole","family":"Liu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4069-4743","authenticated-orcid":false,"given":"Heng","family":"Guo","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6176-3130","authenticated-orcid":false,"given":"Ge","family":"Yang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,10,30]]},"reference":[{"key":"9_CR1","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"174","DOI":"10.1007\/978-3-319-66185-8_20","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2017","author":"L Alvarez","year":"2017","unstructured":"Alvarez, L., et al.: Tracking the aortic lumen geometry by\u00a0optimizing the 3D orientation of\u00a0its\u00a0cross-sections. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017, Part II. LNCS, vol. 10434, pp. 174\u2013181. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-66185-8_20"},{"issue":"5","key":"9_CR2","doi-asserted-by":"publisher","first-page":"674","DOI":"10.1109\/TMI.2003.812261","volume":"22","author":"L Antiga","year":"2003","unstructured":"Antiga, L., Ene-Iordache, B., Remuzzi, A.: Computational geometry for patient-specific reconstruction and meshing of blood vessels from MR and CT angiography. IEEE Trans. Med. Imaging 22(5), 674\u2013684 (2003)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"9_CR3","unstructured":"Ara\u00fajo, R.J., Cardoso, J.S., Oliveira, H.P.: Topological similarity index and loss function for blood vessel segmentation. arXiv preprint arXiv:2107.14531 (2021)"},{"issue":"2","key":"9_CR4","doi-asserted-by":"publisher","first-page":"172","DOI":"10.1016\/j.media.2009.11.003","volume":"14","author":"C Bauer","year":"2010","unstructured":"Bauer, C., Pock, T., Sorantin, E., Bischof, H., Beichel, R.: Segmentation of interwoven 3D tubular tree structures utilizing shape priors and graph cuts. Med. Image Anal. 14(2), 172\u2013184 (2010)","journal-title":"Med. Image Anal."},{"issue":"12","key":"9_CR5","doi-asserted-by":"publisher","first-page":"1247","DOI":"10.1038\/s41592-019-0612-7","volume":"16","author":"JC Caicedo","year":"2019","unstructured":"Caicedo, J.C., et al.: Nucleus segmentation across imaging experiments: the 2018 data science bowl. Nat. Methods 16(12), 1247\u20131253 (2019)","journal-title":"Nat. Methods"},{"key":"9_CR6","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1023\/A:1007979827043","volume":"22","author":"V Caselles","year":"1997","unstructured":"Caselles, V., Kimmel, R., Sapiro, G.: Geodesic active contours. Int. J. Comput. Vision 22, 61\u201379 (1997)","journal-title":"Int. J. Comput. Vision"},{"key":"9_CR7","doi-asserted-by":"publisher","first-page":"1018","DOI":"10.1109\/TMI.2023.3326742","volume":"43","author":"M Challoob","year":"2023","unstructured":"Challoob, M., Gao, Y., Busch, A.: Distinctive phase interdependency model for retinal vasculature delineation in OCT-angiography images. IEEE Trans. Med. Imaging 43, 1018\u20131032 (2023)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"9_CR8","unstructured":"Chen, J., et al.: TransUNet: transformers make strong encoders for medical image segmentation. arXiv preprint arXiv:2102.04306 (2021)"},{"key":"9_CR9","doi-asserted-by":"crossref","unstructured":"Dai, J., et al.: Deformable convolutional networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 764\u2013773 (2017)","DOI":"10.1109\/ICCV.2017.89"},{"key":"9_CR10","doi-asserted-by":"crossref","unstructured":"Dong, S., et al.: DeU-Net 2.0: enhanced deformable U-Net for 3D cardiac cine MRI segmentation. Med. Image Anal. 78, 102389 (2022)","DOI":"10.1016\/j.media.2022.102389"},{"key":"9_CR11","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"386","DOI":"10.1007\/978-3-030-88010-1_32","volume-title":"Pattern Recognition and Computer Vision","author":"Y Guo","year":"2021","unstructured":"Guo, Y., Huang, J., Zhou, Y., Luo, Y., Li, W., Yang, G.: Segmentation of intracellular structures in fluorescence microscopy images by fusing low-level features. In: Ma, H., Wang, L., Zhang, C., Wu, F., Tan, T., Wang, Y., Lai, J., Zhao, Y. (eds.) PRCV 2021, Part III. LNCS, vol. 13021, pp. 386\u2013397. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-88010-1_32"},{"key":"9_CR12","unstructured":"Gupta, S., Zhang, Y., Hu, X., Prasanna, P., Chen, C.: Topology-aware uncertainty for image segmentation. Adv. Neural Inf. Process. Syst. 36 (2024)"},{"key":"9_CR13","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"9_CR14","doi-asserted-by":"crossref","unstructured":"He, Y., et al.: Thin semantics enhancement via high-frequency priori rule for thin structures segmentation. In: 2021 IEEE International Conference on Data Mining (ICDM), pp. 1096\u20131101. IEEE (2021)","DOI":"10.1109\/ICDM51629.2021.00128"},{"issue":"8","key":"9_CR15","doi-asserted-by":"publisher","first-page":"951","DOI":"10.1109\/TMI.2003.815900","volume":"22","author":"A Hoover","year":"2003","unstructured":"Hoover, A., Goldbaum, M.: Locating the optic nerve in a retinal image using the fuzzy convergence of the blood vessels. IEEE Trans. Med. Imaging 22(8), 951\u2013958 (2003)","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"6","key":"9_CR16","doi-asserted-by":"publisher","first-page":"900","DOI":"10.1016\/j.cell.2010.02.034","volume":"140","author":"GS Hotamisligil","year":"2010","unstructured":"Hotamisligil, G.S.: Endoplasmic reticulum stress and the inflammatory basis of metabolic disease. Cell 140(6), 900\u2013917 (2010)","journal-title":"Cell"},{"key":"9_CR17","unstructured":"Hu, X., Li, F., Samaras, D., Chen, C.: Topology-preserving deep image segmentation. Adv. Neural Inf. Process. Syst. 32 (2019)"},{"key":"9_CR18","unstructured":"Hu, X., Wang, Y., Fuxin, L., Samaras, D., Chen, C.: Topology-aware segmentation using discrete Morse theory. arXiv preprint arXiv:2103.09992 (2021)"},{"key":"9_CR19","doi-asserted-by":"publisher","first-page":"66","DOI":"10.1109\/LSP.2019.2956367","volume":"27","author":"Y Huang","year":"2019","unstructured":"Huang, Y., Tang, Z., Chen, D., Su, K., Chen, C.: Batching soft IoU for training semantic segmentation networks. IEEE Signal Process. Lett. 27, 66\u201370 (2019)","journal-title":"IEEE Signal Process. Lett."},{"issue":"2","key":"9_CR20","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1038\/s41592-020-01008-z","volume":"18","author":"F Isensee","year":"2021","unstructured":"Isensee, F., Jaeger, P.F., Kohl, S.A., Petersen, J., Maier-Hein, K.H.: nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nat. Methods 18(2), 203\u2013211 (2021)","journal-title":"Nat. Methods"},{"key":"9_CR21","doi-asserted-by":"publisher","first-page":"149","DOI":"10.1016\/j.knosys.2019.04.025","volume":"178","author":"Q Jin","year":"2019","unstructured":"Jin, Q., Meng, Z., Pham, T.D., Chen, Q., Wei, L., Su, R.: DUNet: a deformable network for retinal vessel segmentation. Knowl.-Based Syst. 178, 149\u2013162 (2019)","journal-title":"Knowl.-Based Syst."},{"issue":"11","key":"9_CR22","doi-asserted-by":"publisher","first-page":"1572","DOI":"10.1109\/83.799885","volume":"8","author":"LM Kaplan","year":"1999","unstructured":"Kaplan, L.M.: Extended fractal analysis for texture classification and segmentation. IEEE Trans. Image Process. 8(11), 1572\u20131585 (1999)","journal-title":"IEEE Trans. Image Process."},{"issue":"12","key":"9_CR23","doi-asserted-by":"publisher","first-page":"3526","DOI":"10.1109\/78.340789","volume":"42","author":"LM Kaplan","year":"1994","unstructured":"Kaplan, L.M., Kuo, C.C.: Extending self-similarity for fractional Brownian motion. IEEE Trans. Signal Process. 42(12), 3526\u20133530 (1994)","journal-title":"IEEE Trans. Signal Process."},{"key":"9_CR24","doi-asserted-by":"crossref","unstructured":"Konatar, I., Popovic, T., Popovic, N.: Box-counting method in Python for fractal analysis of biomedical images. In: 2020 24th International Conference on Information Technology (IT), pp.\u00a01\u20134. IEEE (2020)","DOI":"10.1109\/IT48810.2020.9070454"},{"key":"9_CR25","doi-asserted-by":"publisher","first-page":"2061","DOI":"10.1109\/TMI.2024.3354408","volume":"43","author":"L Kreitner","year":"2024","unstructured":"Kreitner, L., et al.: Synthetic optical coherence tomography angiographs for detailed retinal vessel segmentation without human annotations. IEEE Trans. Med. Imaging 43, 2061\u20132073 (2024)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"9_CR26","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2023.102986","volume":"90","author":"G La Barbera","year":"2023","unstructured":"La Barbera, G., et al.: Tubular structures segmentation of pediatric abdominal-visceral ceCT images with renal tumors: assessment, comparison and improvement. Med. Image Anal. 90, 102986 (2023)","journal-title":"Med. Image Anal."},{"key":"9_CR27","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1007\/s001380050121","volume":"12","author":"I Laptev","year":"2000","unstructured":"Laptev, I., Mayer, H., Lindeberg, T., Eckstein, W., Steger, C., Baumgartner, A.: Automatic extraction of roads from aerial images based on scale space and snakes. Mach. Vis. Appl. 12, 23\u201331 (2000)","journal-title":"Mach. Vis. Appl."},{"key":"9_CR28","doi-asserted-by":"crossref","unstructured":"Lee, C.A., Blackstone, C.: ER morphology and endo-lysosomal crosstalk: functions and disease implications. Biochimica et Biophysica Acta (BBA)-Mol. Cell Biol. Lipids 1865(1), 158544 (2020)","DOI":"10.1016\/j.bbalip.2019.158544"},{"issue":"6","key":"9_CR29","doi-asserted-by":"publisher","first-page":"1894","DOI":"10.1016\/j.sigpro.2009.12.010","volume":"90","author":"WL Lee","year":"2010","unstructured":"Lee, W.L., Hsieh, K.S.: A robust algorithm for the fractal dimension of images and its applications to the classification of natural images and ultrasonic liver images. Signal Process. 90(6), 1894\u20131904 (2010)","journal-title":"Signal Process."},{"issue":"11","key":"9_CR30","doi-asserted-by":"publisher","first-page":"2460","DOI":"10.1016\/j.patcog.2009.03.001","volume":"42","author":"J Li","year":"2009","unstructured":"Li, J., Du, Q., Sun, C.: An improved box-counting method for image fractal dimension estimation. Pattern Recogn. 42(11), 2460\u20132469 (2009)","journal-title":"Pattern Recogn."},{"issue":"9","key":"9_CR31","doi-asserted-by":"publisher","first-page":"5826","DOI":"10.1109\/TCYB.2022.3194099","volume":"53","author":"Y Li","year":"2023","unstructured":"Li, Y., et al.: Global transformer and dual local attention network via deep-shallow hierarchical feature fusion for retinal vessel segmentation. IEEE Trans. Cybernet. 53(9), 5826\u20135839 (2023)","journal-title":"IEEE Trans. Cybernet."},{"issue":"12","key":"9_CR32","doi-asserted-by":"publisher","first-page":"3279","DOI":"10.1016\/j.patcog.2013.06.017","volume":"46","author":"PL Lin","year":"2013","unstructured":"Lin, P.L., Huang, P.W., Lee, C.H., Wu, M.T.: Automatic classification for solitary pulmonary nodule in CT image by fractal analysis based on fractional Brownian motion model. Pattern Recogn. 46(12), 3279\u20133287 (2013)","journal-title":"Pattern Recogn."},{"issue":"3","key":"9_CR33","doi-asserted-by":"publisher","first-page":"117","DOI":"10.1016\/j.cmpb.2015.05.004","volume":"121","author":"P Lin","year":"2015","unstructured":"Lin, P., Huang, P., Huang, P., Hsu, H.: Alveolar bone-loss area localization in periodontitis radiographs based on threshold segmentation with a hybrid feature fused of intensity and the h-value of fractional Brownian motion model. Comput. Methods Programs Biomed. 121(3), 117\u2013126 (2015)","journal-title":"Comput. Methods Programs Biomed."},{"key":"9_CR34","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431\u20133440 (2015)","DOI":"10.1109\/CVPR.2015.7298965"},{"issue":"4","key":"9_CR35","doi-asserted-by":"publisher","first-page":"634","DOI":"10.1016\/j.media.2009.05.003","volume":"13","author":"R Lopes","year":"2009","unstructured":"Lopes, R., Betrouni, N.: Fractal and multifractal analysis: a review. Med. Image Anal. 13(4), 634\u2013649 (2009)","journal-title":"Med. Image Anal."},{"key":"9_CR36","doi-asserted-by":"publisher","unstructured":"Luo, Y., Guo, Y., Li, W., Liu, G., Yang, G.: Fluorescence microscopy image datasets for deep learning segmentation of intracellular orgenelle networks (2020). https:\/\/doi.org\/10.21227\/t2he-zn97. https:\/\/dx.doi.org\/10.21227\/t2he-zn97","DOI":"10.21227\/t2he-zn97"},{"issue":"3","key":"9_CR37","doi-asserted-by":"publisher","first-page":"928","DOI":"10.1109\/TMI.2020.3042802","volume":"40","author":"Y Ma","year":"2020","unstructured":"Ma, Y., et al.: ROSE: a retinal OCT-angiography vessel segmentation dataset and new model. IEEE Trans. Med. Imaging 40(3), 928\u2013939 (2020)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"9_CR38","unstructured":"Mnih, V.: Machine Learning for Aerial Image Labeling. University of Toronto (Canada) (2013)"},{"key":"9_CR39","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"721","DOI":"10.1007\/978-3-030-32239-7_80","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019","author":"L Mou","year":"2019","unstructured":"Mou, L., et al.: CS-Net: channel and spatial attention network for curvilinear structure segmentation. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11764, pp. 721\u2013730. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32239-7_80"},{"key":"9_CR40","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1007\/978-3-540-30135-6_7","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2004","author":"D Nain","year":"2004","unstructured":"Nain, D., Yezzi, A., Turk, G.: Vessel segmentation using a shape driven flow. In: Barillot, C., Haynor, D.R., Hellier, P. (eds.) MICCAI 2004. LNCS, vol. 3216, pp. 51\u201359. Springer, Heidelberg (2004). https:\/\/doi.org\/10.1007\/978-3-540-30135-6_7"},{"key":"9_CR41","doi-asserted-by":"publisher","first-page":"661","DOI":"10.1109\/TPAMI.1984.4767591","volume":"6","author":"AP Pentland","year":"1984","unstructured":"Pentland, A.P.: Fractal-based description of natural scenes. IEEE Trans. Pattern Anal. Mach. Intell. 6, 661\u2013674 (1984)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"9_CR42","doi-asserted-by":"crossref","unstructured":"Qi, Y., He, Y., Qi, X., Zhang, Y., Yang, G.: Dynamic snake convolution based on topological geometric constraints for tubular structure segmentation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 6070\u20136079 (2023)","DOI":"10.1109\/ICCV51070.2023.00558"},{"key":"9_CR43","doi-asserted-by":"crossref","unstructured":"Roberto, G.F., Lumini, A., Neves, L.A., do\u00a0Nascimento, M.Z.: Fractal neural network: a new ensemble of fractal geometry and convolutional neural networks for the classification of histology images. Expert Syst. Appl. 166, 114103 (2021)","DOI":"10.1016\/j.eswa.2020.114103"},{"key":"9_CR44","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1007\/978-3-319-24574-4_28","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015","author":"O Ronneberger","year":"2015","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015, Part III. LNCS, vol. 9351, pp. 234\u2013241. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28"},{"issue":"8","key":"9_CR45","doi-asserted-by":"publisher","first-page":"4006","DOI":"10.1109\/JBHI.2023.3274789","volume":"27","author":"T Shi","year":"2023","unstructured":"Shi, T., et al.: Affinity feature strengthening for accurate, complete and robust vessel segmentation. IEEE J. Biomed. Health Inform. 27(8), 4006\u20134017 (2023)","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"9_CR46","doi-asserted-by":"crossref","unstructured":"Shit, S., et al.: clDice-a novel topology-preserving loss function for tubular structure segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 16560\u201316569 (2021)","DOI":"10.1109\/CVPR46437.2021.01629"},{"key":"9_CR47","doi-asserted-by":"crossref","unstructured":"Sironi, A., Lepetit, V., Fua, P.: Multiscale centerline detection by learning a scale-space distance transform. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2697\u20132704 (2014)","DOI":"10.1109\/CVPR.2014.351"},{"issue":"1","key":"9_CR48","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12880-015-0068-x","volume":"15","author":"AA Taha","year":"2015","unstructured":"Taha, A.A., Hanbury, A.: Metrics for evaluating 3D medical image segmentation: analysis, selection, and tool. BMC Med. Imaging 15(1), 1\u201328 (2015)","journal-title":"BMC Med. Imaging"},{"key":"9_CR49","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"348","DOI":"10.1007\/978-3-030-32226-7_39","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019","author":"C Wang","year":"2019","unstructured":"Wang, C., et al.: Tubular structure segmentation using spatial fully connected network with radial distance loss for 3D medical images. In: Shen, D., et al. (eds.) MICCAI 2019, Part VI. LNCS, vol. 11769, pp. 348\u2013356. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32226-7_39"},{"issue":"10","key":"9_CR50","doi-asserted-by":"publisher","first-page":"3349","DOI":"10.1109\/TPAMI.2020.2983686","volume":"43","author":"J Wang","year":"2020","unstructured":"Wang, J., et al.: Deep high-resolution representation learning for visual recognition. IEEE Trans. Pattern Anal. Mach. Intell. 43(10), 3349\u20133364 (2020)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"9_CR51","doi-asserted-by":"crossref","unstructured":"Wang, Y., et al.: Deep distance transform for tubular structure segmentation in CT scans. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 3833\u20133842 (2020)","DOI":"10.1109\/CVPR42600.2020.00389"},{"key":"9_CR52","doi-asserted-by":"crossref","unstructured":"Wong, C.C., Vong, C.M.: Persistent homology based graph convolution network for fine-grained 3D shape segmentation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 7098\u20137107 (2021)","DOI":"10.1109\/ICCV48922.2021.00701"},{"issue":"4","key":"9_CR53","doi-asserted-by":"publisher","first-page":"1427","DOI":"10.1109\/JBHI.2018.2872813","volume":"23","author":"Z Yan","year":"2018","unstructured":"Yan, Z., Yang, X., Cheng, K.T.: A three-stage deep learning model for accurate retinal vessel segmentation. IEEE J. Biomed. Health Inform. 23(4), 1427\u20131436 (2018)","journal-title":"IEEE J. Biomed. Health Inform."},{"issue":"11","key":"9_CR54","doi-asserted-by":"publisher","first-page":"15593","DOI":"10.1007\/s11042-022-12418-w","volume":"81","author":"X Yang","year":"2022","unstructured":"Yang, X., Li, Z., Guo, Y., Zhou, D.: DCU-Net: a deformable convolutional neural network based on cascade U-Net for retinal vessel segmentation. Multimedia Tools Appl. 81(11), 15593\u201315607 (2022)","journal-title":"Multimedia Tools Appl."},{"key":"9_CR55","doi-asserted-by":"crossref","unstructured":"Yang, Z., Farsiu, S.: Directional connectivity-based segmentation of medical images. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 11525\u201311535 (2023)","DOI":"10.1109\/CVPR52729.2023.01109"},{"issue":"12","key":"9_CR56","doi-asserted-by":"publisher","first-page":"1411","DOI":"10.1109\/42.974935","volume":"20","author":"PJ Yim","year":"2001","unstructured":"Yim, P.J., Cebral, J.J., Mullick, R., Marcos, H.B., Choyke, P.L.: Vessel surface reconstruction with a tubular deformable model. IEEE Trans. Med. Imaging 20(12), 1411\u20131421 (2001)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"9_CR57","doi-asserted-by":"crossref","unstructured":"Yu, F., Koltun, V., Funkhouser, T.: Dilated residual networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 472\u2013480 (2017)","DOI":"10.1109\/CVPR.2017.75"},{"issue":"2","key":"9_CR58","doi-asserted-by":"publisher","first-page":"438","DOI":"10.1109\/TMI.2017.2756073","volume":"37","author":"Y Zhao","year":"2017","unstructured":"Zhao, Y., et al.: Automatic 2-D\/3-D vessel enhancement in multiple modality images using a weighted symmetry filter. IEEE Trans. Med. Imaging 37(2), 438\u2013450 (2017)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"9_CR59","doi-asserted-by":"crossref","unstructured":"Zhou, Y., Huang, J., Wang, C., Song, L., Yang, G.: XNet: wavelet-based low and high frequency fusion networks for fully- and semi-supervised semantic segmentation of biomedical images. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 21085\u201321096, October 2023","DOI":"10.1109\/ICCV51070.2023.01928"},{"key":"9_CR60","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/978-3-030-00889-5_1","volume-title":"Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support","author":"Z Zhou","year":"2018","unstructured":"Zhou, Z., Rahman Siddiquee, M.M., Tajbakhsh, N., Liang, J.: UNet++: a nested U-Net architecture for medical image segmentation. In: Stoyanov, D., et al. (eds.) DLMIA\/ML-CDS -2018. LNCS, vol. 11045, pp. 3\u201311. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00889-5_1"},{"key":"9_CR61","doi-asserted-by":"publisher","first-page":"623","DOI":"10.1007\/s11517-018-1907-z","volume":"57","author":"Z Zhuang","year":"2019","unstructured":"Zhuang, Z., Lei, N., Joseph Raj, A.N., Qiu, S.: Application of fractal theory and fuzzy enhancement in ultrasound image segmentation. Med. Biol. Eng. Comput. 57, 623\u2013632 (2019)","journal-title":"Med. Biol. Eng. Comput."}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2024"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-73404-5_9","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,24]],"date-time":"2025-04-24T19:44:36Z","timestamp":1745523876000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-73404-5_9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,30]]},"ISBN":["9783031734038","9783031734045"],"references-count":61,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-73404-5_9","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,10,30]]},"assertion":[{"value":"30 October 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Computer Vision","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Milan","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 September 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 October 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2024.ecva.net\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}