{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T23:59:19Z","timestamp":1773878359459,"version":"3.50.1"},"publisher-location":"Cham","reference-count":20,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031164484","type":"print"},{"value":"9783031164491","type":"electronic"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"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":[[2022]]},"DOI":"10.1007\/978-3-031-16449-1_34","type":"book-chapter","created":{"date-parts":[[2022,9,16]],"date-time":"2022-09-16T08:04:54Z","timestamp":1663315494000},"page":"355-364","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Rethinking Surgical Instrument Segmentation: A Background Image Can Be All You Need"],"prefix":"10.1007","author":[{"given":"An","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mobarakol","family":"Islam","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mengya","family":"Xu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongliang","family":"Ren","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,9,17]]},"reference":[{"key":"34_CR1","unstructured":"Allan, M., et al.: 2018 robotic scene segmentation challenge (2020)"},{"key":"34_CR2","unstructured":"Allan, M., et al: 2017 robotic instrument segmentation challenge (2019)"},{"key":"34_CR3","doi-asserted-by":"crossref","unstructured":"Cao, B., Zhang, H., Wang, N., Gao, X., Shen, D.: Auto-gan: self-supervised collaborative learning for medical image synthesis. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 10486\u201310493 (2020)","DOI":"10.1609\/aaai.v34i07.6619"},{"key":"34_CR4","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"700","DOI":"10.1007\/978-3-030-59716-0_67","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2020","author":"E Colleoni","year":"2020","unstructured":"Colleoni, E., Edwards, P., Stoyanov, D.: Synthetic and real inputs for tool segmentation in robotic surgery. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12263, pp. 700\u2013710. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-59716-0_67"},{"issue":"4","key":"34_CR5","doi-asserted-by":"publisher","first-page":"897","DOI":"10.1007\/s00345-019-02898-1","volume":"38","author":"RW Dobbs","year":"2020","unstructured":"Dobbs, R.W., Halgrimson, W.R., Talamini, S., Vigneswaran, H.T., Wilson, J.O., Crivellaro, S.: Single-port robotic surgery: the next generation of minimally invasive urology. World J. Urol. 38(4), 897\u2013905 (2020)","journal-title":"World J. Urol."},{"issue":"10","key":"34_CR6","doi-asserted-by":"publisher","first-page":"78","DOI":"10.1145\/2347736.2347755","volume":"55","author":"P Domingos","year":"2012","unstructured":"Domingos, P.: A few useful things to know about machine learning. Commun. ACM 55(10), 78\u201387 (2012)","journal-title":"Commun. ACM"},{"key":"34_CR7","unstructured":"Eilertsen, G., Tsirikoglou, A., Lundstr\u00f6m, C., Unger, J.: Ensembles of gans for synthetic training data generation (2021)"},{"issue":"5","key":"34_CR8","doi-asserted-by":"publisher","first-page":"1450","DOI":"10.1109\/TMI.2021.3057884","volume":"40","author":"LC Garcia-Peraza-Herrera","year":"2021","unstructured":"Garcia-Peraza-Herrera, L.C., Fidon, L., D\u2019Ettorre, C., Stoyanov, D., Vercauteren, T., Ourselin, S.: Image compositing for segmentation of surgical tools without manual annotations. IEEE Trans. Med. Imaging 40(5), 1450\u20131460 (2021)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"34_CR9","volume-title":"Deep Learning","author":"I Goodfellow","year":"2016","unstructured":"Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT press, Cambridge (2016)"},{"key":"34_CR10","doi-asserted-by":"crossref","unstructured":"Hamghalam, M., Lei, B., Wang, T.: High tissue contrast MRI synthesis using multi-stage attention-gan for segmentation. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 4067\u20134074 (2020)","DOI":"10.1609\/aaai.v34i04.5825"},{"key":"34_CR11","doi-asserted-by":"crossref","unstructured":"Han, C., et al.: Synthesizing diverse lung nodules wherever massively: 3d multi-conditional gan-based CT image augmentation for object detection. In: 2019 International Conference on 3D Vision (3DV), pp. 729\u2013737. IEEE (2019)","DOI":"10.1109\/3DV.2019.00085"},{"key":"34_CR12","unstructured":"Hendrycks, D., Mu, N., Cubuk, E.D., Zoph, B., Gilmer, J., Lakshminarayanan, B.: Augmix: a simple data processing method to improve robustness and uncertainty. arXiv preprint arXiv:1912.02781 (2019)"},{"key":"34_CR13","unstructured":"Jung, A.B., et al.: imgaug. https:\/\/github.com\/aleju\/imgaug. Accessed 01 Feb 2020 (2020)"},{"key":"34_CR14","doi-asserted-by":"crossref","unstructured":"Kishore, A., Choe, T.E., Kwon, J., Park, M., Hao, P., Mittel, A.: Synthetic data generation using imitation training. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 3078\u20133086 (2021)","DOI":"10.1109\/ICCVW54120.2021.00342"},{"key":"34_CR15","unstructured":"Madan, S., et al.: When and how do cnns generalize to out-of-distribution category-viewpoint combinations? arXiv preprint arXiv:2007.08032 (2020)"},{"key":"34_CR16","unstructured":"Paszke, A., et al.: Automatic differentiation in pytorch. In: NIPS-W (2017)"},{"key":"34_CR17","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1007\/978-3-319-24574-4_28","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015","author":"O Ronneberger","year":"2015","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234\u2013241. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28"},{"key":"34_CR18","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/978-3-030-00536-8_1","volume-title":"Simulation and Synthesis in Medical Imaging","author":"H-C Shin","year":"2018","unstructured":"Shin, H.-C., et al.: Medical image synthesis for data augmentation and anonymization using generative adversarial networks. In: Gooya, A., Goksel, O., Oguz, I., Burgos, N. (eds.) SASHIMI 2018. LNCS, vol. 11037, pp. 1\u201311. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00536-8_1"},{"key":"34_CR19","doi-asserted-by":"crossref","unstructured":"Tremblay, J., et al.: Training deep networks with synthetic data: bridging the reality gap by domain randomization. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 969\u2013977 (2018)","DOI":"10.1109\/CVPRW.2018.00143"},{"key":"34_CR20","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"269","DOI":"10.1007\/978-3-030-87202-1_26","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2021","author":"M Xu","year":"2021","unstructured":"Xu, M., Islam, M., Lim, C.M., Ren, H.: Class-incremental domain adaptation with smoothing and calibration for\u00a0surgical report generation. In: de Bruijne, M., Cattin, P.C., Cotin, S., Padoy, N., Speidel, S., Zheng, Y., Essert, C. (eds.) MICCAI 2021. LNCS, vol. 12904, pp. 269\u2013278. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-87202-1_26"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2022"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-16449-1_34","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,7]],"date-time":"2024-03-07T16:56:35Z","timestamp":1709830595000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-16449-1_34"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031164484","9783031164491"],"references-count":20,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-16449-1_34","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"17 September 2022","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":"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":"22 September 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2022","order":10,"name":"conference_id","label":"Conference ID","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 Conference","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1831","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":"574","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":"31% - 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)"}}]}}