{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,2]],"date-time":"2025-10-02T22:40:15Z","timestamp":1759444815594,"version":"build-2065373602"},"publisher-location":"Cham","reference-count":19,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030597153"},{"type":"electronic","value":"9783030597160"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-030-59716-0_36","type":"book-chapter","created":{"date-parts":[[2020,10,2]],"date-time":"2020-10-02T20:03:41Z","timestamp":1601669021000},"page":"375-384","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["AutoSNAP: Automatically Learning Neural Architectures for Instrument Pose Estimation"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4101-819X","authenticated-orcid":false,"given":"David","family":"K\u00fcgler","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Marc","family":"Uecker","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6413-0061","authenticated-orcid":false,"given":"Arjan","family":"Kuijper","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0669-4018","authenticated-orcid":false,"given":"Anirban","family":"Mukhopadhyay","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,9,29]]},"reference":[{"key":"36_CR1","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"228","DOI":"10.1007\/978-3-030-32245-8_26","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019","author":"W Bae","year":"2019","unstructured":"Bae, W., Lee, S., Lee, Y., Park, B., Chung, M., Jung, K.-H.: Resource optimized neural architecture search for 3D medical image segmentation. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11765, pp. 228\u2013236. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32245-8_26"},{"issue":"8","key":"36_CR2","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 (2019)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"36_CR3","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"828","DOI":"10.1007\/978-3-030-32226-7_92","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019","author":"N Dong","year":"2019","unstructured":"Dong, N., Xu, M., Liang, X., Jiang, Y., Dai, W., Xing, E.: Neural architecture search for adversarial medical image segmentation. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11769, pp. 828\u2013836. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32226-7_92"},{"key":"36_CR4","doi-asserted-by":"crossref","unstructured":"Elsken, T., Metzen, J.H., Hutter, F.: Neural architecture search: a survey 20, 1\u201321 (2019). http:\/\/jmlr.org\/papers\/v20\/18-598.html","DOI":"10.1007\/978-3-030-05318-5_11"},{"key":"36_CR5","first-page":"24","volume":"52","author":"HA Hajj","year":"2019","unstructured":"Hajj, H.A., et al.: CATARACTS: challenge on automatic tool annotation for cataract surgery. Med. IA 52, 24\u201341 (2019)","journal-title":"Med. IA"},{"key":"36_CR6","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"220","DOI":"10.1007\/978-3-030-32248-9_25","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019","author":"S Kim","year":"2019","unstructured":"Kim, S., et al.: Scalable neural architecture search for 3D medical image segmentation. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11766, pp. 220\u2013228. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32248-9_25"},{"key":"36_CR7","doi-asserted-by":"publisher","unstructured":"K\u00fcgler, D., et al.: i3posnet: instrument pose estimation from x-ray in temporal bone surgery. Int. J. Comput. Assist. Radiol. Surg. 15(7), 1137\u20131145 (2020). https:\/\/doi.org\/10.1007\/s11548-020-02157-4","DOI":"10.1007\/s11548-020-02157-4"},{"key":"36_CR8","unstructured":"Liu, H., Simonyan, K., Yang, Y.: DARTS: differentiable architecture search. In: ICLR 2019 (2019). https:\/\/arxiv.org\/pdf\/1806.09055"},{"key":"36_CR9","unstructured":"Luo, R., Tian, F., Qin, T., Chen, E., Liu, T.Y.: Neural architecture optimization. In: Bengio, S., et al. (eds.) Advances in NeurIPS, vol. 31. Curran Associates, Inc. (2018)"},{"issue":"9","key":"36_CR10","doi-asserted-by":"publisher","first-page":"691","DOI":"10.1038\/s41551-017-0132-7","volume":"1","author":"L Maier-Hein","year":"2017","unstructured":"Maier-Hein, L., et al.: Surgical data science for next-generation interventions. Nat. BioMed. Eng. 1(9), 691\u2013696 (2017)","journal-title":"Nat. BioMed. Eng."},{"issue":"5","key":"36_CR11","doi-asserted-by":"publisher","first-page":"1352","DOI":"10.1109\/TMI.2016.2521800","volume":"35","author":"S Miao","year":"2016","unstructured":"Miao, S., Wang, Z.J., Liao, R.: A CNN regression approach for real-time 2D\/3D registration. IEEE Trans. Med. Imaging 35(5), 1352\u20131363 (2016)","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"10","key":"36_CR12","doi-asserted-by":"publisher","first-page":"764","DOI":"10.1258\/0022215042450643","volume":"118","author":"J Schipper","year":"2004","unstructured":"Schipper, J., et al.: Navigation as a quality management tool in cochlear implant surgery. J. Laryngol. Otol. 118(10), 764\u2013770 (2004)","journal-title":"J. Laryngol. Otol."},{"issue":"1","key":"36_CR13","doi-asserted-by":"publisher","first-page":"86","DOI":"10.1109\/TMI.2016.2593957","volume":"36","author":"AP Twinanda","year":"2017","unstructured":"Twinanda, A.P., Shehata, S., Mutter, D., Marescaux, J., de Mathelin, M., Padoy, N.: EndoNet: a deep architecture for recognition tasks on laparoscopic videos. IEEE Trans. Med. Imaging 36(1), 86\u201397 (2017)","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"9","key":"36_CR14","doi-asserted-by":"publisher","first-page":"1517","DOI":"10.1007\/s11548-019-02011-2","volume":"14","author":"M Unberath","year":"2019","unstructured":"Unberath, M., et al.: Enabling machine learning in x-ray-based procedures via realistic simulation of image formation. Int. J. Comput. Assist. Radiol. Surg. 14(9), 1517\u20131528 (2019). https:\/\/doi.org\/10.1007\/s11548-019-02011-2","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"issue":"1","key":"36_CR15","doi-asserted-by":"publisher","first-page":"198","DOI":"10.1109\/JPROC.2019.2946993","volume":"108","author":"T Vercauteren","year":"2020","unstructured":"Vercauteren, T., Unberath, M., Padoy, N., Navab, N.: CAI4CAI: the rise of contextual artificial intelligence in computer assisted interventions. Proc. IEEE 108(1), 198\u2013214 (2020). https:\/\/doi.org\/10.1109\/JPROC.2019.2946993","journal-title":"Proc. IEEE"},{"key":"36_CR16","doi-asserted-by":"publisher","first-page":"44247","DOI":"10.1109\/ACCESS.2019.2908991","volume":"7","author":"Y Weng","year":"2019","unstructured":"Weng, Y., Zhou, T., Li, Y., Qiu, X.: NAS-Unet: neural architecture search for medical image segmentation. IEEE Access 7, 44247\u201344257 (2019)","journal-title":"IEEE Access"},{"key":"36_CR17","doi-asserted-by":"crossref","unstructured":"Yu, Q., et al.: C2FNAS: coarse-to-fine neural architecture search for 3D medical image segmentation (2019). https:\/\/arxiv.org\/pdf\/1912.09628","DOI":"10.1109\/CVPR42600.2020.00418"},{"key":"36_CR18","doi-asserted-by":"crossref","unstructured":"Zhu, Z., Liu, C., Yang, D., Yuille, A., Xu, D.: V-NAS: neural architecture search for volumetric medical image segmentation. In: 2019 International Conference on 3D Vision, pp. 240\u2013248. IEEE Computer Society, Conference Publishing Services, Los Alamitos (2019)","DOI":"10.1109\/3DV.2019.00035"},{"key":"36_CR19","doi-asserted-by":"crossref","unstructured":"Zoph, B., Vasudevan, V., Shlens, J., Le, V.Q.: Learning transferable architectures for scalable image recognition. In: Brown, M.S., et al. (eds.) CVPR Proceedings (2018)","DOI":"10.1109\/CVPR.2018.00907"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2020"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-59716-0_36","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,2]],"date-time":"2025-10-02T22:03:29Z","timestamp":1759442609000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-59716-0_36"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030597153","9783030597160"],"references-count":19,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-59716-0_36","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"29 September 2020","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":"Lima","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Peru","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 October 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 October 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.miccai2020.org\/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":"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":"1809","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":"542","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":"30% - 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":"4","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)"}},{"value":"The conference was held virtually due to the COVID-19 pandemic.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}