{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,17]],"date-time":"2025-12-17T23:10:06Z","timestamp":1766013006130,"version":"3.40.3"},"publisher-location":"Cham","reference-count":34,"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_4","type":"book-chapter","created":{"date-parts":[[2024,2,4]],"date-time":"2024-02-04T17:02:21Z","timestamp":1707066141000},"page":"35-45","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["3D Inception-Based TransMorph: Pre- and Post-operative Multi-contrast MRI Registration in Brain Tumors"],"prefix":"10.1007","author":[{"given":"Javid","family":"Abderezaei","sequence":"first","affiliation":[]},{"given":"Aymeric","family":"Pionteck","sequence":"additional","affiliation":[]},{"given":"Agamdeep","family":"Chopra","sequence":"additional","affiliation":[]},{"given":"Mehmet","family":"Kurt","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,2,5]]},"reference":[{"issue":"17","key":"4_CR1","doi-asserted-by":"publisher","first-page":"4799","DOI":"10.1088\/0031-9155\/59\/17\/4799","volume":"59","author":"H Dang","year":"2014","unstructured":"Dang, H., Wang, A.S., Sussman, M.S., Siewerdsen, J.H., Stayman, J.W.: DPIRPLE: a joint estimation framework for deformable registration and penalized-likelihood CT image reconstruction using prior images. Phys. Med. Biol. 59(17), 4799 (2014)","journal-title":"Phys. Med. Biol."},{"issue":"11","key":"4_CR2","doi-asserted-by":"publisher","first-page":"4273","DOI":"10.1088\/1361-6560\/aa6070","volume":"62","author":"JR McClelland","year":"2017","unstructured":"McClelland, J.R., et al.: A generalized framework unifying image registration and respiratory motion models and incorporating image reconstruction, for partial image data or full images. Phys. Med. Biol. 62(11), 4273 (2017)","journal-title":"Phys. Med. Biol."},{"key":"4_CR3","doi-asserted-by":"crossref","unstructured":"Yang, X., Rossi, P.J., Jani, A.B., Mao, H., Curran, W.J., Liu, T.: \u201c3D transrectal ultrasound (TRUS) prostate segmentation based on optimal feature learning framework,\u201d In: Medical Imaging 2016: Image Processing, vol. 9784, pp. 654\u2013660 (2016)","DOI":"10.1117\/12.2216396"},{"issue":"7","key":"4_CR4","doi-asserted-by":"publisher","first-page":"2812","DOI":"10.1088\/1361-6560\/aa6055","volume":"62","author":"Y Fu","year":"2017","unstructured":"Fu, Y., Liu, S., Li, H.H., Yang, D.: Automatic and hierarchical segmentation of the human skeleton in CT images. Phys. Med. Biol. 62(7), 2812 (2017)","journal-title":"Phys. Med. Biol."},{"key":"4_CR5","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"428","DOI":"10.1007\/978-3-642-23623-5_54","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2011","author":"YB Fu","year":"2011","unstructured":"Fu, Y.B., Chui, C.K., Teo, C.L., Kobayashi, E.: Motion tracking and strain map computation for quasi-static magnetic resonance elastography. In: Fichtinger, G., Martel, A., Peters, T. (eds.) MICCAI 2011. LNCS, vol. 6891, pp. 428\u2013435. Springer, Heidelberg (2011). https:\/\/doi.org\/10.1007\/978-3-642-23623-5_54"},{"key":"4_CR6","doi-asserted-by":"crossref","unstructured":"Yang, X., Ghafourian, P., Sharma, P., Salman, K., Martin, D., Fei, B.: \u201cNonrigid registration and classification of the kidneys in 3D dynamic contrast enhanced (DCE) MR images,\u201d In: Medical Imaging 2012: Image Processing, 2012, vol. 8314, pp. 105\u2013112 (2012)","DOI":"10.1117\/12.912190"},{"key":"4_CR7","doi-asserted-by":"publisher","first-page":"1657","DOI":"10.1007\/978-3-319-32552-1_63","volume-title":"Springer handbook of robotics","author":"RH Taylor","year":"2016","unstructured":"Taylor, R.H., Menciassi, A., Fichtinger, G., Fiorini, P., Dario, P.: Medical robotics and computer-integrated surgery. In: Siciliano, B., Khatib, O. (eds.) Springer handbook of robotics, pp. 1657\u20131684. Springer International Publishing, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-32552-1_63"},{"issue":"4","key":"4_CR8","doi-asserted-by":"publisher","first-page":"285","DOI":"10.1078\/0939-3889-00327","volume":"16","author":"D Sarrut","year":"2006","unstructured":"Sarrut, D.: Deformable registration for image-guided radiation therapy. Z. F\u00fcr Med. Phys. 16(4), 285\u2013297 (2006)","journal-title":"Z. F\u00fcr Med. Phys."},{"issue":"8","key":"4_CR9","doi-asserted-by":"publisher","first-page":"3009","DOI":"10.1088\/0031-9155\/61\/8\/3009","volume":"61","author":"T De Silva","year":"2016","unstructured":"De Silva, T., et al.: 3D\u20132D image registration for target localization in spine surgery: investigation of similarity metrics providing robustness to content mismatch. Phys. Med. Biol. 61(8), 3009 (2016)","journal-title":"Phys. Med. Biol."},{"key":"4_CR10","doi-asserted-by":"publisher","unstructured":"Gaser, C.: Structural MRI: Morphometry. In:Neuroeconomics, M. Reuter and C. Montag, Eds. Berlin, Heidelberg: Springer  (2016). https:\/\/doi.org\/10.1007\/978-3-642-35923-1_21","DOI":"10.1007\/978-3-642-35923-1_21"},{"key":"4_CR11","doi-asserted-by":"publisher","unstructured":"Ashburner, J.: A fast diffeomorphic image registration algorithm. Neuroimage 38(1), 95\u2013113 (2007). https:\/\/doi.org\/10.1016\/j.neuroimage.2007.07.007","DOI":"10.1016\/j.neuroimage.2007.07.007"},{"issue":"1","key":"4_CR12","doi-asserted-by":"publisher","first-page":"26","DOI":"10.1016\/j.media.2007.06.004","volume":"12","author":"BB Avants","year":"2008","unstructured":"Avants, B.B., Epstein, C.L., Grossman, M., Gee, J.C.: Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain. Med. Image Anal. 12(1), 26\u201341 (2008). https:\/\/doi.org\/10.1016\/j.media.2007.06.004","journal-title":"Med. Image Anal."},{"issue":"4","key":"4_CR13","doi-asserted-by":"publisher","first-page":"985","DOI":"10.1109\/TMM.2017.2759508","volume":"20","author":"J Li","year":"2018","unstructured":"Li, J., Liang, X., Shen, S., Xu, T., Feng, J., Yan, S.: Scale-aware fast R-CNN for pedestrian detection. IEEE Trans. Multimed. 20(4), 985\u2013996 (2018). https:\/\/doi.org\/10.1109\/TMM.2017.2759508","journal-title":"IEEE Trans. Multimed."},{"key":"4_CR14","doi-asserted-by":"crossref","unstructured":"Wu, Y., et al.: \u201cRethinking Classification and Localization for Object Detection,\u201d 2020, pp. 10186\u201310195 (2020). Accessed 15 Jul 2022. https:\/\/openaccess.thecvf.com\/content_CVPR_2020\/html\/Wu_Rethinking_Classification_and_Localization_for_Object_Detection_CVPR_2020_paper.html","DOI":"10.1109\/CVPR42600.2020.01020"},{"key":"4_CR15","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, vol. 25 (2012). Accessed 15 Jul 2022. https:\/\/proceedings.neurips.cc\/paper\/2012\/hash\/c399862d3b9d6b76c8436e924a68c45b-Abstract.html"},{"key":"4_CR16","doi-asserted-by":"publisher","unstructured":"Xie, Q., Luong, M.T., Hovy, E., Le, Q.V.: Self-training with noisy student improves imagenet classification. In: 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA, Jun. 2020, pp. 10684\u201310695. https:\/\/doi.org\/10.1109\/CVPR42600.2020.01070","DOI":"10.1109\/CVPR42600.2020.01070"},{"key":"4_CR17","unstructured":"Tao, A., Sapra, K., Catanzaro, B.: Hierarchical multi-scale attention for semantic segmentation. ArXiv Prepr. arXiv:200510821 (2020)"},{"key":"4_CR18","doi-asserted-by":"crossref","unstructured":"Chen, L.C., Yang, Y., Wang, J., Xu, W., Yuille, A.L.: Attention to scale: scale-aware semantic image segmentation (2016), pp. 3640\u20133649. Accessed Jul 15 2022. [Online]. Available: https:\/\/openaccess.thecvf.com\/content_cvpr_2016\/html\/Chen_Attention_to_Scale_CVPR_2016_paper.html","DOI":"10.1109\/CVPR.2016.396"},{"key":"4_CR19","doi-asserted-by":"publisher","first-page":"378","DOI":"10.1016\/j.neuroimage.2017.07.008","volume":"158","author":"X Yang","year":"2017","unstructured":"Yang, X., Kwitt, R., Styner, M., Niethammer, M.: Quicksilver: fast predictive image registration \u2013 a deep learning approach. Neuroimage 158, 378\u2013396 (2017). https:\/\/doi.org\/10.1016\/j.neuroimage.2017.07.008","journal-title":"Neuroimage"},{"key":"4_CR20","doi-asserted-by":"publisher","unstructured":"Dalca, A.V., Balakrishnan, G., Guttag, J., Sabuncu, M.R.: Unsupervised Learning for Fast Probabilistic Diffeomorphic Registration. arXiv, 2018, Accessed: Jul 15 2022 https:\/\/doi.org\/10.1007\/978-3-030-00928-1_82 https:\/\/dspace.mit.edu\/handle\/1721.1\/137585","DOI":"10.1007\/978-3-030-00928-1_82"},{"key":"4_CR21","doi-asserted-by":"publisher","unstructured":"Balakrishnan, G., Zhao, A., Sabuncu, M.R., Guttag, J., Dalca, A.V.: An unsupervised learning model for deformable medical image registration. ArXiv180202604 Cs (2018) https:\/\/doi.org\/10.1109\/CVPR.2018.00964","DOI":"10.1109\/CVPR.2018.00964"},{"issue":"8","key":"4_CR22","doi-asserted-by":"publisher","first-page":"1788","DOI":"10.1109\/TMI.2019.2897538","volume":"38","author":"G Balakrishnan","year":"2019","unstructured":"Balakrishnan, G., Zhao, A., Sabuncu, M.R., Guttag, J., Dalca, A.V.: VoxelMorph: a learning framework for deformable medical image registration. IEEE Trans. Med. Imaging 38(8), 1788\u20131800 (Aug.2019). https:\/\/doi.org\/10.1109\/TMI.2019.2897538","journal-title":"IEEE Trans. Med. Imaging"},{"key":"4_CR23","doi-asserted-by":"publisher","unstructured":"Estienne, T., et al.: U-ReSNet: Ultimate Coupling of Registration and Segmentation with Deep Nets. In: Shen, D., Liu, T., Peters, T.M., Staib, L.H., Essert, C., Zhou, S., Yap, P.-T., Khan, A. (eds.) MICCAI 2019. LNCS, vol. 11766, pp. 310\u2013319. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32248-9_35","DOI":"10.1007\/978-3-030-32248-9_35"},{"key":"4_CR24","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"300","DOI":"10.1007\/978-3-319-66182-7_35","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2212 MICCAI 2017","author":"X Cao","year":"2017","unstructured":"Cao, X., Yang, J., Zhang, J., Nie, D., Kim, M., Wang, Q., Shen, D.: Deformable Image Registration Based on Similarity-Steered CNN Regression. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10433, pp. 300\u2013308. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-66182-7_35"},{"key":"4_CR25","doi-asserted-by":"publisher","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 (2021). https:\/\/doi.org\/10.48550\/arXiv.2112.06979","DOI":"10.48550\/arXiv.2112.06979"},{"key":"4_CR26","doi-asserted-by":"crossref","unstructured":"Chen, J., Frey, E.C., He, Y., Segars, W.P., Li, Y., Du, Y.: TransMorph: Transformer for unsupervised medical image registration (2022). Accessed 15 Jul 2022. http:\/\/arxiv.org\/abs\/2111.10480","DOI":"10.1016\/j.media.2022.102615"},{"key":"4_CR27","unstructured":"Szegedy, C., et al.: Going Deeper with Convolutions (2014). Accessed 15 Jul 2022. http:\/\/arxiv.org\/abs\/1409.4842"},{"key":"4_CR28","unstructured":"Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, vol. 30 (2017). Accessed 21 Apr 2022. https:\/\/papers.nips.cc\/paper\/2017\/hash\/3f5ee243547dee91fbd053c1c4a845aa-Abstract.html"},{"key":"4_CR29","unstructured":"Dosovitskiy, A., et al.: An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. ArXiv201011929 Cs, Jun 2021, Accessed: Apr. 21 2022. http:\/\/arxiv.org\/abs\/2010.11929"},{"key":"4_CR30","unstructured":"Chen, J., He, Y., Frey, E.C., Li, Y., Du, Y.: ViT-V-Net: Vision transformer for unsupervised volumetric medical image registration. ArXiv210406468 Cs Eess, Apr 2021, Accessed 21 Apr 2022. http:\/\/arxiv.org\/abs\/2104.06468"},{"key":"4_CR31","doi-asserted-by":"crossref","unstructured":"Liu, Z., et al.: Swin transformer: hierarchical vision transformer using shifted windows 2021, pp. 10012\u201310022 (2021). Accessed Jul 22 2022. https:\/\/openaccess.thecvf.com\/content\/ICCV2021\/html\/Liu_Swin_Transformer_Hierarchical_Vision_Transformer_Using_Shifted_Windows_ICCV_2021_paper.html","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"4_CR32","unstructured":"Paszke, A., et al.: PyTorch: an imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems, 2019, vol. 32 (2019). Accessed 22 Jul 2022. https:\/\/proceedings.neurips.cc\/paper\/2019\/hash\/bdbca288fee7f92f2bfa9f7012727740-Abstract.html"},{"issue":"2","key":"4_CR33","doi-asserted-by":"publisher","first-page":"358","DOI":"10.1109\/4.996","volume":"23","author":"N Kanopoulos","year":"1988","unstructured":"Kanopoulos, N., Vasanthavada, N., Baker, R.L.: Design of an image edge detection filter using the Sobel operator. IEEE J. Solid-State Circuits 23(2), 358\u2013367 (1988). https:\/\/doi.org\/10.1109\/4.996","journal-title":"IEEE J. Solid-State Circuits"},{"key":"4_CR34","unstructured":"Xu, Z., Wu, Z., Feng, J.: CFUN: combining faster R-CNN and U-net network for efficient whole heart segmentation (2018)"}],"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_4","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,4]],"date-time":"2024-02-04T17:02:56Z","timestamp":1707066176000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-44153-0_4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031441523","9783031441530"],"references-count":34,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-44153-0_4","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)"}}]}}