{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T08:04:39Z","timestamp":1773302679659,"version":"3.50.1"},"publisher-location":"Cham","reference-count":28,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031438943","type":"print"},{"value":"9783031438950","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-3-031-43895-0_72","type":"book-chapter","created":{"date-parts":[[2023,9,30]],"date-time":"2023-09-30T23:08:57Z","timestamp":1696115337000},"page":"765-775","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["DeDA: Deep Directed Accumulator"],"prefix":"10.1007","author":[{"given":"Hang","family":"Zhang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rongguang","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Renjiu","family":"Hu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jinwei","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiahao","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,10,1]]},"reference":[{"issue":"5","key":"72_CR1","doi-asserted-by":"publisher","first-page":"669","DOI":"10.1002\/ana.23959","volume":"74","author":"M Absinta","year":"2013","unstructured":"Absinta, M., et al.: Seven-tesla phase imaging of acute multiple sclerosis lesions: a new window into the inflammatory process. Ann. Neurol. 74(5), 669\u2013678 (2013)","journal-title":"Ann. Neurol."},{"issue":"7","key":"72_CR2","doi-asserted-by":"publisher","first-page":"2597","DOI":"10.1172\/JCI86198","volume":"126","author":"M Absinta","year":"2016","unstructured":"Absinta, M., et al.: Persistent 7-tesla phase rim predicts poor outcome in new multiple sclerosis patient lesions. J. Clin. Investig. 126(7), 2597\u20132609 (2016)","journal-title":"J. Clin. Investig."},{"issue":"2","key":"72_CR3","doi-asserted-by":"publisher","first-page":"111","DOI":"10.1016\/0031-3203(81)90009-1","volume":"13","author":"DH Ballard","year":"1981","unstructured":"Ballard, D.H.: Generalizing the Hough transform to detect arbitrary shapes. Pattern Recogn. 13(2), 111\u2013122 (1981)","journal-title":"Pattern Recogn."},{"key":"72_CR4","doi-asserted-by":"crossref","unstructured":"Barquero, G., et al.: RimNet: a deep 3D multimodal MRI architecture for paramagnetic rim lesion assessment in multiple sclerosis. NeuroImage Clinical 28, 102412 (2020)","DOI":"10.1016\/j.nicl.2020.102412"},{"issue":"3","key":"72_CR5","doi-asserted-by":"publisher","first-page":"103","DOI":"10.1145\/1276377.1276506","volume":"26","author":"Jiawen Chen","year":"2007","unstructured":"Chen, Jiawen, Paris, Sylvain, Durand, Fr\u00e9do.: Real-time edge-aware image processing with the bilateral grid. ACM Trans. Graph. 26(3), 103 (2007). https:\/\/doi.org\/10.1145\/1276377.1276506","journal-title":"ACM Trans. Graph."},{"issue":"1","key":"72_CR6","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1007\/s00401-016-1636-z","volume":"133","author":"A Dal-Bianco","year":"2017","unstructured":"Dal-Bianco, A., et al.: Slow expansion of multiple sclerosis iron rim lesions: pathology and 7 t magnetic resonance imaging. Acta Neuropathol. 133(1), 25\u201342 (2017)","journal-title":"Acta Neuropathol."},{"issue":"1","key":"72_CR7","doi-asserted-by":"publisher","first-page":"194","DOI":"10.1002\/mrm.22187","volume":"63","author":"L De Rochefort","year":"2010","unstructured":"De Rochefort, L., et al.: Quantitative susceptibility map reconstruction from MR phase data using Bayesian regularization: validation and application to brain imaging. Magn. Reson. Med. Official J. Int. Soc. Magn. Reson. Med. 63(1), 194\u2013206 (2010)","journal-title":"Magn. Reson. Med. Official J. Int. Soc. Magn. Reson. Med."},{"key":"72_CR8","unstructured":"Dosovitskiy, A., et al.: An image is worth 16 $$\\times $$ 16 words: transformers for image recognition at scale. In: International Conference on Learning Representations (2020)"},{"key":"72_CR9","doi-asserted-by":"crossref","unstructured":"Gillen, K.M., et al.: QSM is an imaging biomarker for chronic glial activation in multiple sclerosis lesions. Ann. Clin. Transl. Neurol. 8(4), 877\u2013886 (2021)","DOI":"10.1002\/acn3.51338"},{"key":"72_CR10","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":"72_CR11","unstructured":"Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. In: International Conference on Machine Learning, pp. 448\u2013456. PMLR (2015)"},{"key":"72_CR12","unstructured":"Jaderberg, M., Simonyan, K., Zisserman, A., et al.: Spatial transformer networks. In: Advances in Neural Information Processing Systems, vol. 28 (2015)"},{"key":"72_CR13","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1016\/j.media.2016.10.004","volume":"36","author":"K Kamnitsas","year":"2017","unstructured":"Kamnitsas, K., et al.: Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation. Med. Image Anal. 36, 61\u201378 (2017)","journal-title":"Med. Image Anal."},{"issue":"1","key":"72_CR14","doi-asserted-by":"publisher","first-page":"133","DOI":"10.1093\/brain\/awy296","volume":"142","author":"UW Kaunzner","year":"2019","unstructured":"Kaunzner, U.W., et al.: Quantitative susceptibility mapping identifies inflammation in a subset of chronic multiple sclerosis lesions. Brain 142(1), 133\u2013145 (2019)","journal-title":"Brain"},{"key":"72_CR15","unstructured":"Kayhan, O.S., Gemert, J.C.V.: On translation invariance in CNNs: convolutional layers can exploit absolute spatial location. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 14274\u201314285 (2020)"},{"key":"72_CR16","doi-asserted-by":"crossref","unstructured":"Lenc, K., Vedaldi, A.: Understanding image representations by measuring their equivariance and equivalence. In: Proceedings of the IEEE Conference On Computer Vision and Pattern Recognition, pp. 991\u2013999 (2015)","DOI":"10.1109\/CVPR.2015.7298701"},{"key":"72_CR17","doi-asserted-by":"crossref","unstructured":"Liu, Z., et al.: Swin transformer: hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 10012\u201310022 (2021)","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"72_CR18","doi-asserted-by":"crossref","unstructured":"Lou, C., et al.: Fully automated detection of paramagnetic rims in multiple sclerosis lesions on 3t susceptibility-based MR imaging. NeuroImage Clin. 32, 102796 (2021)","DOI":"10.1016\/j.nicl.2021.102796"},{"key":"72_CR19","doi-asserted-by":"crossref","unstructured":"Matsoukas, C., Haslum, J.F., Sorkhei, M., S\u00f6derberg, M., Smith, K.: What makes transfer learning work for medical images: feature reuse and other factors. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 9225\u20139234 (2022)","DOI":"10.1109\/CVPR52688.2022.00901"},{"issue":"9","key":"72_CR20","doi-asserted-by":"publisher","first-page":"2306","DOI":"10.1109\/TMI.2021.3075856","volume":"40","author":"M Muckley","year":"2021","unstructured":"Muckley, M., et al.: Results of the 2020 fastMRI challenge for machine learning MR image reconstruction. IEEE Trans. Med. Imaging 40(9), 2306\u20132317 (2021)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"72_CR21","doi-asserted-by":"publisher","first-page":"2557","DOI":"10.1109\/TIP.2022.3155954","volume":"31","author":"T Shi","year":"2022","unstructured":"Shi, T., Boutry, N., Xu, Y., G\u00e9raud, T.: Local intensity order transformation for robust curvilinear object segmentation. IEEE Trans. Image Process. 31, 2557\u20132569 (2022)","journal-title":"IEEE Trans. Image Process."},{"issue":"1","key":"72_CR22","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1002\/mrm.25358","volume":"73","author":"Y Wang","year":"2015","unstructured":"Wang, Y., Liu, T.: Quantitative susceptibility mapping (QSM): decoding MRI data for a tissue magnetic biomarker. Magn. Reson. Med. 73(1), 82\u2013101 (2015)","journal-title":"Magn. Reson. Med."},{"key":"72_CR23","unstructured":"Zhang, H., Hu, R., Chen, X., Wang, R., Zhang, J., Li, J.: DAGrid: directed accumulator grid. arXiv preprint arXiv:2306.02589 (2023)"},{"key":"72_CR24","doi-asserted-by":"crossref","unstructured":"Zhang, H., et al.: QSMRim-Net: imbalance-aware learning for identification of chronic active multiple sclerosis lesions on quantitative susceptibility maps. NeuroImage Clin. 34, 102979 (2022)","DOI":"10.1016\/j.nicl.2022.102979"},{"key":"72_CR25","doi-asserted-by":"crossref","unstructured":"Zhang, H., et al.: ALL-Net: anatomical information lesion-wise loss function integrated into neural network for multiple sclerosis lesion segmentation. NeuroImage Clin. 32, 102854 (2021)","DOI":"10.1016\/j.nicl.2021.102854"},{"key":"72_CR26","doi-asserted-by":"crossref","unstructured":"Zhang, H., et al.: Geometric loss for deep multiple sclerosis lesion segmentation. In: 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), pp. 24\u201328. IEEE (2021)","DOI":"10.1109\/ISBI48211.2021.9434085"},{"key":"72_CR27","doi-asserted-by":"crossref","unstructured":"Zhang, H., et al.: Efficient folded attention for medical image reconstruction and segmentation. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 10868\u201310876 (2021)","DOI":"10.1609\/aaai.v35i12.17298"},{"key":"72_CR28","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Zhang, H., Zhao, L., Chen, T., Arik, S.\u00d6., Pfister, T.: Nested hierarchical transformer: towards accurate, data-efficient and interpretable visual understanding. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 36, pp. 3417\u20133425 (2022)","DOI":"10.1609\/aaai.v36i3.20252"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2023"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-43895-0_72","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,11]],"date-time":"2024-03-11T14:36:05Z","timestamp":1710167765000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-43895-0_72"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031438943","9783031438950"],"references-count":28,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-43895-0_72","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"1 October 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MICCAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Medical Image Computing and Computer-Assisted Intervention","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Vancouver, BC","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Canada","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 October 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 October 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/conferences.miccai.org\/2023\/en\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2250","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"730","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"32% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"5","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}