{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T22:01:30Z","timestamp":1743026490061,"version":"3.40.3"},"publisher-location":"Cham","reference-count":21,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031441523"},{"type":"electronic","value":"9783031441530"}],"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\/978-3-031-44153-0_2","type":"book-chapter","created":{"date-parts":[[2024,2,4]],"date-time":"2024-02-04T17:02:21Z","timestamp":1707066141000},"page":"15-24","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["WSSAMNet: Weakly Supervised Semantic Attentive Medical Image Registration Network"],"prefix":"10.1007","author":[{"given":"Sahar","family":"Almahfouz Nasser","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nikhil Cherian","family":"Kurian","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mohit","family":"Meena","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Saqib","family":"Shamsi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Amit","family":"Sethi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,2,5]]},"reference":[{"key":"2_CR1","unstructured":"Baheti, B., et al.: The brain tumor sequence registration challenge: establishing correspondence between pre-operative and follow-up MRI scans of diffuse glioma patients. arXiv preprint: arXiv:2112.06979 (2021)"},{"key":"2_CR2","doi-asserted-by":"publisher","first-page":"757","DOI":"10.1109\/TMI.2020.3021387","volume":"41","author":"RJ Chen","year":"2020","unstructured":"Chen, R.J., et al.: Pathomic fusion: an integrated framework for fusing histopathology and genomic features for cancer diagnosis and prognosis. IEEE Trans. Med. Imaging 41, 757\u2013770 (2020)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"2_CR3","unstructured":"Crawford, R.: Automated image stitching using sift feature matching (2012)"},{"key":"2_CR4","doi-asserted-by":"publisher","unstructured":"Fedorov, A., Nguyen, P.L., Tuncali, K., Tempany, C.: Annotated MRI and ultrasound volume images of the prostate (2015). https:\/\/doi.org\/10.5281\/zenodo.16396","DOI":"10.5281\/zenodo.16396"},{"issue":"1","key":"2_CR5","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1023\/A:1021897212261","volume":"18","author":"B Fischer","year":"2003","unstructured":"Fischer, B., Modersitzki, J.: Curvature based image registration. J. Math. Imaging Vis. 18(1), 81\u201385 (2003)","journal-title":"J. Math. Imaging Vis."},{"key":"2_CR6","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"80","DOI":"10.1007\/978-3-030-71827-5_10","volume-title":"Segmentation, Classification, and Registration of Multi-modality Medical Imaging Data","author":"N Gunnarsson","year":"2021","unstructured":"Gunnarsson, N., Sj\u00f6lund, J., Sch\u00f6n, T.B.: Learning a deformable registration pyramid. In: Shusharina, N., Heinrich, M.P., Huang, R. (eds.) MICCAI 2020. LNCS, vol. 12587, pp. 80\u201386. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-71827-5_10"},{"key":"2_CR7","unstructured":"Guo, C.K.: Multi-modal image registration with unsupervised deep learning. Ph.D. thesis, Massachusetts Institute of Technology (2019)"},{"key":"2_CR8","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"105","DOI":"10.1007\/978-3-030-11723-8_10","volume-title":"Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries","author":"X Han","year":"2019","unstructured":"Han, X., et al.: Patient-specific registration of pre-operative and post-recurrence brain tumor MRI scans. In: Crimi, A., Bakas, S., Kuijf, H., Keyvan, F., Reyes, M., van Walsum, T. (eds.) BrainLes 2018. LNCS, vol. 11383, pp. 105\u2013114. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-11723-8_10"},{"key":"2_CR9","doi-asserted-by":"crossref","unstructured":"Hu, Y., et al.: Label-driven weakly-supervised learning for multimodal deformable image registration. In: 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), pp. 1070\u20131074. IEEE (2018)","DOI":"10.1109\/ISBI.2018.8363756"},{"key":"2_CR10","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.media.2018.07.002","volume":"49","author":"Y Hu","year":"2018","unstructured":"Hu, Y., et al.: Weakly-supervised convolutional neural networks for multimodal image registration. Med. Image Anal. 49, 1\u201313 (2018)","journal-title":"Med. Image Anal."},{"issue":"1","key":"2_CR11","doi-asserted-by":"publisher","first-page":"196","DOI":"10.1109\/TMI.2009.2035616","volume":"29","author":"S Klein","year":"2009","unstructured":"Klein, S., Staring, M., Murphy, K., Viergever, M.A., Pluim, J.P.: Elastix: a toolbox for intensity-based medical image registration. IEEE Trans. Med. Imaging 29(1), 196\u2013205 (2009)","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"1","key":"2_CR12","first-page":"17","volume":"5","author":"TX Lin","year":"2016","unstructured":"Lin, T.X., Chang, H.H.: Medical image registration based on an improved ant colony optimization algorithm. Int. J. Pharma. Med. Biol. Sci. 5(1), 17\u201322 (2016)","journal-title":"Int. J. Pharma. Med. Biol. Sci."},{"key":"2_CR13","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Goyal, P., Girshick, R., He, K., Doll\u00e1r, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980\u20132988 (2017)","DOI":"10.1109\/ICCV.2017.324"},{"key":"2_CR14","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"165","DOI":"10.1007\/978-3-030-01045-4_20","volume-title":"Simulation, Image Processing, and Ultrasound Systems for Assisted Diagnosis and Navigation","author":"I Machado","year":"2018","unstructured":"Machado, I., et al.: Deformable MRI-ultrasound registration via attribute matching and mutual-saliency weighting for image-guided neurosurgery. In: Stoyanov, D., et al. (eds.) POCUS\/BIVPCS\/CuRIOUS\/CPM -2018. LNCS, vol. 11042, pp. 165\u2013171. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01045-4_20"},{"key":"2_CR15","doi-asserted-by":"crossref","unstructured":"Mahapatra, D., Antony, B., Sedai, S., Garnavi, R.: Deformable medical image registration using generative adversarial networks. In: 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), pp. 1449\u20131453. IEEE (2018)","DOI":"10.1109\/ISBI.2018.8363845"},{"key":"2_CR16","first-page":"33","volume":"2010","author":"M Modat","year":"2010","unstructured":"Modat, M., McClelland, J., Ourselin, S.: Lung registration using the NiftyReg package. Med. Image Anal. Clin.-a Grand Challenge 2010, 33\u201342 (2010)","journal-title":"Med. Image Anal. Clin.-a Grand Challenge"},{"key":"2_CR17","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1007\/978-3-030-59716-0_21","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2020","author":"TCW Mok","year":"2020","unstructured":"Mok, T.C.W., Chung, A.C.S.: Large deformation diffeomorphic image registration with Laplacian Pyramid networks. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12263, pp. 211\u2013221. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-59716-0_21"},{"key":"2_CR18","unstructured":"Noble, J.A.: Reflections on ultrasound image analysis (2016)"},{"key":"2_CR19","doi-asserted-by":"publisher","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation (2015). https:\/\/doi.org\/10.48550\/ARXIV.1505.04597","DOI":"10.48550\/ARXIV.1505.04597"},{"key":"2_CR20","unstructured":"Schwarz, L.A.: Non-rigid registration using free-form deformations. Technische Universit\u00e4t M\u00fcnchen 6, 4 (2007)"},{"issue":"11","key":"2_CR21","doi-asserted-by":"publisher","first-page":"977","DOI":"10.1016\/S0262-8856(03)00137-9","volume":"21","author":"B Zitova","year":"2003","unstructured":"Zitova, B., Flusser, J.: Image registration methods: a survey. Image Vis. Comput. 21(11), 977\u20131000 (2003)","journal-title":"Image Vis. Comput."}],"container-title":["Lecture Notes in Computer Science","Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-44153-0_2","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,4]],"date-time":"2024-02-04T17:02:37Z","timestamp":1707066157000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-44153-0_2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031441523","9783031441530"],"references-count":21,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-44153-0_2","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"5 February 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"BrainLes","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International MICCAI Brainlesion Workshop","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Singapore","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Singapore","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 September 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 September 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iwb2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.brainlesion-workshop.org\/","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":"Microsoft CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"65","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":"46","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":"71% - 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":"1-2","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)"}}]}}