{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,26]],"date-time":"2025-06-26T08:52:41Z","timestamp":1750927961898,"version":"3.40.3"},"publisher-location":"Cham","reference-count":37,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031752902"},{"type":"electronic","value":"9783031752919"}],"license":[{"start":{"date-parts":[[2024,10,26]],"date-time":"2024-10-26T00:00:00Z","timestamp":1729900800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,10,26]],"date-time":"2024-10-26T00:00:00Z","timestamp":1729900800000},"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\/978-3-031-75291-9_7","type":"book-chapter","created":{"date-parts":[[2024,10,25]],"date-time":"2024-10-25T20:16:25Z","timestamp":1729887385000},"page":"84-96","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Robust Curve Detection in\u00a0Volumetric Medical Imaging via\u00a0Attraction Field"],"prefix":"10.1007","author":[{"given":"Farukh","family":"Yaushev","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Daria","family":"Nogina","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Valentin","family":"Samokhin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mariya","family":"Dugova","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ekaterina","family":"Petrash","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dmitry","family":"Sevryukov","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mikhail","family":"Belyaev","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Maxim","family":"Pisov","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,10,26]]},"reference":[{"issue":"2","key":"7_CR1","doi-asserted-by":"publisher","first-page":"915","DOI":"10.1118\/1.3528204","volume":"38","author":"SG Armato","year":"2011","unstructured":"Armato, S.G., et al.: The lung image database consortium (LIDC) and image database resource initiative (IDRI): a completed reference database of lung nodules on CT scans. Med. Phys. 38(2), 915\u2013931 (2011)","journal-title":"Med. Phys."},{"key":"7_CR2","unstructured":"Bakas, S., et\u00a0al.: Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the brats challenge. arXiv preprint arXiv:1811.02629 (2018)"},{"key":"7_CR3","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"717","DOI":"10.1007\/978-3-319-46478-7_44","volume-title":"Computer Vision \u2013 ECCV 2016","author":"A Bulat","year":"2016","unstructured":"Bulat, A., Tzimiropoulos, G.: Human pose estimation via convolutional part heatmap regression. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016, Part VII. LNCS, vol. 9911, pp. 717\u2013732. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46478-7_44"},{"key":"7_CR4","doi-asserted-by":"crossref","unstructured":"Canny, J.: A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. PAMI-8(6), 679\u2013698 (1986). https:\/\/doi.org\/10.1109\/TPAMI.1986.4767851","DOI":"10.1109\/TPAMI.1986.4767851"},{"key":"7_CR5","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1016\/j.media.2017.07.008","volume":"42","author":"F Gr\u00e9lard","year":"2017","unstructured":"Gr\u00e9lard, F., Baldacci, F., Vialard, A., Domenger, J.P.: New methods for the geometrical analysis of tubular organs. Med. Image Anal. 42, 89\u2013101 (2017)","journal-title":"Med. Image Anal."},{"issue":"11492","key":"7_CR6","first-page":"441","volume":"2019","author":"Z Guo","year":"2019","unstructured":"Guo, Z., et al.: Deepcenterline: a multi-task fully convolutional network for centerline extraction. IPMI 2019(11492), 441\u2013453 (2019)","journal-title":"IPMI"},{"issue":"12","key":"7_CR7","doi-asserted-by":"publisher","DOI":"10.1118\/1.4901412","volume":"41","author":"L Hadjiiski","year":"2014","unstructured":"Hadjiiski, L., et al.: Ureter tracking and segmentation in CT urography (CTU) using compass. Med. Phys. 41(12), 121906 (2014)","journal-title":"Med. Phys."},{"key":"7_CR8","doi-asserted-by":"crossref","unstructured":"Hahn, L.D., et\u00a0al.: CT-based true-and false-lumen segmentation in type B aortic dissection using machine learning. Radiol. Cardiothoracic Imaging 2(3), e190179 (2020)","DOI":"10.1148\/ryct.2020190179"},{"key":"7_CR9","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1007\/978-3-030-59725-2_3","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2020","author":"J He","year":"2020","unstructured":"He, J., et al.: Learning hybrid representations for automatic 3D vessel centerline extraction. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12266, pp. 24\u201334. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-59725-2_3"},{"key":"7_CR10","unstructured":"Ji, Y., et\u00a0al.: AMOS: a large-scale abdominal multi-organ benchmark for versatile medical image segmentation. arXiv preprint arXiv:2206.08023 (2022)"},{"key":"7_CR11","unstructured":"Le, H., Borji, A.: What are the receptive, effective receptive, and projective fields of neurons in convolutional neural networks? arXiv preprint arXiv:1705.07049 (2017)"},{"key":"7_CR12","doi-asserted-by":"crossref","unstructured":"L\u00f6ffler, M., et\u00a0al.: A vertebral segmentation dataset with fracture grading. Radiol. Artif. Intell. 2(4), e190138 (2020)","DOI":"10.1148\/ryai.2020190138"},{"key":"7_CR13","doi-asserted-by":"crossref","unstructured":"Milletari, F., Navab, N., Ahmadi, S.A.: V-Net: fully convolutional neural networks for volumetric medical image segmentation. In: 3DV, pp. 565\u2013571 (2016)","DOI":"10.1109\/3DV.2016.79"},{"issue":"7","key":"7_CR14","first-page":"3523","volume":"44","author":"S Minaee","year":"2021","unstructured":"Minaee, S., Boykov, Y., Porikli, F., Plaza, A., Kehtarnavaz, N., Terzopoulos, D.: Image segmentation using deep learning: a survey. IEEE Trans. Pattern Anal. Mach. Intell. 44(7), 3523\u20133542 (2021)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"7_CR15","unstructured":"Nan, X., Bai, S., Wang, F., Xia, G.S., Wu, T., Zhang, L.: Learning attraction field representation for robust line segment detection. In: CVPR (2019)"},{"key":"7_CR16","doi-asserted-by":"publisher","unstructured":"Neubeck, A., Gool, L.V.: Efficient non-maximum suppression. In: ICPR 2006, vol. 3, pp. 850\u2013855 (2006). https:\/\/doi.org\/10.1109\/ICPR.2006.479","DOI":"10.1109\/ICPR.2006.479"},{"key":"7_CR17","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"483","DOI":"10.1007\/978-3-319-46484-8_29","volume-title":"Computer Vision \u2013 ECCV 2016","author":"A Newell","year":"2016","unstructured":"Newell, A., Yang, K., Deng, J.: Stacked Hourglass networks for human pose estimation. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9912, pp. 483\u2013499. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46484-8_29"},{"key":"7_CR18","doi-asserted-by":"publisher","unstructured":"Nikolov, S., et\u00a0al.: Clinically applicable segmentation of head and neck anatomy for radiotherapy: deep learning algorithm development and validation study. J. Med. Internet Res. 23, e26151 (2021). https:\/\/doi.org\/10.2196\/26151","DOI":"10.2196\/26151"},{"key":"7_CR19","doi-asserted-by":"crossref","unstructured":"Pautrat, R., Barath, D., Larsson, V., Oswald, M.R., Pollefeys, M.: DeepLSD: line segment detection and refinement with deep image gradients. arXiv preprint arXiv:2212.07766 (2022)","DOI":"10.1109\/CVPR52729.2023.01662"},{"key":"7_CR20","unstructured":"Poma, X.S., Riba, E., Sappa, A.: Dense extreme inception network: towards a robust cnn model for edge detection. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp. 1923\u20131932 (2020)"},{"key":"7_CR21","unstructured":"Roug\u00e9, P., Passat, N., Merveille, O.: Cascaded multitask U-Net using topological loss for vessel segmentation and centerline extraction. arXiv preprint arXiv:2307.11603 (2023)"},{"key":"7_CR22","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.media.2017.06.015","volume":"42","author":"A Setio","year":"2017","unstructured":"Setio, A., et al.: Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: the LUNA16 challenge. Med. Image Anal. 42, 1\u201313 (2017)","journal-title":"Med. Image Anal."},{"key":"7_CR23","doi-asserted-by":"publisher","unstructured":"Shit, S., et\u00a0al.: 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). https:\/\/doi.org\/10.1109\/CVPR46437.2021.01629","DOI":"10.1109\/CVPR46437.2021.01629"},{"issue":"4","key":"7_CR24","doi-asserted-by":"publisher","first-page":"284","DOI":"10.1006\/cbmr.1996.0021","volume":"29","author":"T Spencer","year":"1996","unstructured":"Spencer, T., Olson, J.A., McHardy, K.C., Sharp, P.F., Forrester, J.V.: An image-processing strategy for the segmentation and quantification of microaneurysms in fluorescein angiograms of the ocular fundus. Comput. Biomed. Res. 29(4), 284\u2013302 (1996)","journal-title":"Comput. Biomed. Res."},{"issue":"5500","key":"7_CR25","doi-asserted-by":"publisher","first-page":"2319","DOI":"10.1126\/science.290.5500.2319","volume":"290","author":"JB Tenenbaum","year":"2000","unstructured":"Tenenbaum, J.B., Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319\u20132323 (2000). https:\/\/doi.org\/10.1126\/science.290.5500.2319","journal-title":"Science"},{"key":"7_CR26","doi-asserted-by":"publisher","unstructured":"Valente, M., Stanciulescu, B.: Real-time method for general road segmentation. In: 2017 IEEE Intelligent Vehicles Symposium (IV), pp. 443\u2013447 (2017). https:\/\/doi.org\/10.1109\/IVS.2017.7995758","DOI":"10.1109\/IVS.2017.7995758"},{"issue":"4","key":"7_CR27","doi-asserted-by":"publisher","first-page":"722","DOI":"10.1109\/TPAMI.2008.300","volume":"32","author":"RG Von Gioi","year":"2008","unstructured":"Von Gioi, R.G., Jakubowicz, J., Morel, J.M., Randall, G.: LSD: a fast line segment detector with a false detection control. IEEE Trans. Pattern Anal. Mach. Intell. 32(4), 722\u2013732 (2008)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"7_CR28","doi-asserted-by":"crossref","unstructured":"Wang, F., Zheng, K., Lu, L., Xiao, J., Wu, M., Miao, S.: Automatic vertebra localization and identification in CT by spine rectification and anatomically-constrained optimization. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 5280\u20135288 (2021)","DOI":"10.1109\/CVPR46437.2021.00524"},{"key":"7_CR29","doi-asserted-by":"publisher","first-page":"679","DOI":"10.3389\/fneur.2018.00679","volume":"9","author":"S Winzeck","year":"2018","unstructured":"Winzeck, S., et al.: ISLES 2016 and 2017-benchmarking ischemic stroke lesion outcome prediction based on multispectral MRI. Front. Neurol. 9, 679 (2018)","journal-title":"Front. Neurol."},{"key":"7_CR30","doi-asserted-by":"crossref","unstructured":"Xie, S., Tu, Z.: Holistically-nested edge detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1395\u20131403 (2015)","DOI":"10.1109\/ICCV.2015.164"},{"key":"7_CR31","doi-asserted-by":"crossref","unstructured":"Xu, Y., Xu, W., Cheung, D., Tu, Z.: Line segment detection using transformers without edges. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 4257\u20134266 (2021)","DOI":"10.1109\/CVPR46437.2021.00424"},{"key":"7_CR32","doi-asserted-by":"crossref","unstructured":"Xue, N., et al.: Holistically-attracted wireframe parsing. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2788\u20132797 (2020)","DOI":"10.1109\/CVPR42600.2020.00286"},{"key":"7_CR33","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2022.102646","volume":"83","author":"A Zakharov","year":"2023","unstructured":"Zakharov, A., et al.: Interpretable vertebral fracture quantification via anchor-free landmarks localization. Med. Image Anal. 83, 102646 (2023)","journal-title":"Med. Image Anal."},{"issue":"9","key":"7_CR34","doi-asserted-by":"publisher","first-page":"1358","DOI":"10.1016\/j.medengphy.2013.03.005","volume":"35","author":"H Zhang","year":"2013","unstructured":"Zhang, H., Kheyfets, V.O., Finol, E.A.: Robust infrarenal aortic aneurysm lumen centerline detection for rupture status classification. Med. Eng. Phys. 35(9), 1358\u20131367 (2013)","journal-title":"Med. Eng. Phys."},{"key":"7_CR35","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"502","DOI":"10.1007\/978-3-030-01246-5_30","volume-title":"Computer Vision \u2013 ECCV 2018","author":"J Zhang","year":"2018","unstructured":"Zhang, J., Xu, Y., Ni, B., Duan, Z.: Geometric constrained joint lane segmentation and lane boundary\u00a0detection. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11205, pp. 502\u2013518. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01246-5_30"},{"key":"7_CR36","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"755","DOI":"10.1007\/978-3-030-00937-3_86","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2018","author":"P Zhang","year":"2018","unstructured":"Zhang, P., Wang, F., Zheng, Y.: Deep reinforcement learning for vessel centerline tracing in multi-modality 3D volumes. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-L\u00f3pez, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11073, pp. 755\u2013763. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00937-3_86"},{"issue":"9","key":"7_CR37","doi-asserted-by":"publisher","first-page":"3473","DOI":"10.1109\/JBHI.2021.3068420","volume":"25","author":"J Zhao","year":"2021","unstructured":"Zhao, J., Feng, Q.: Automatic aortic dissection centerline extraction via morphology-guided CRN tracker. IEEE J. Biomed. Health Inform. 25(9), 3473\u20133485 (2021)","journal-title":"IEEE J. Biomed. Health Inform."}],"container-title":["Lecture Notes in Computer Science","Shape in Medical Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-75291-9_7","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,25]],"date-time":"2024-10-25T20:16:59Z","timestamp":1729887419000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-75291-9_7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,26]]},"ISBN":["9783031752902","9783031752919"],"references-count":37,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-75291-9_7","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024,10,26]]},"assertion":[{"value":"26 October 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ShapeMI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Workshop on Shape in Medical Imaging","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Marrakesh","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Morocco","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":"7 October 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7 October 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"shapemi2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/shapemi.github.io\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}