{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T18:01:27Z","timestamp":1743098487227,"version":"3.40.3"},"publisher-location":"Cham","reference-count":31,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031439957"},{"type":"electronic","value":"9783031439964"}],"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\/978-3-031-43996-4_24","type":"book-chapter","created":{"date-parts":[[2023,9,30]],"date-time":"2023-09-30T23:07:48Z","timestamp":1696115268000},"page":"249-259","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Cochlear Implant Fold Detection in Intra-operative CT Using Weakly Supervised Multi-task Deep Learning"],"prefix":"10.1007","author":[{"given":"Mohammad M. R.","family":"Khan","sequence":"first","affiliation":[]},{"given":"Yubo","family":"Fan","sequence":"additional","affiliation":[]},{"given":"Benoit M.","family":"Dawant","sequence":"additional","affiliation":[]},{"given":"Jack H.","family":"Noble","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,10,1]]},"reference":[{"key":"24_CR1","unstructured":"US Department of Health and Human Services, National Institute on Deafness and Other Communication Disorders, Cochlear implants, No. 11\u20134798 (2014)"},{"issue":"3","key":"24_CR2","doi-asserted-by":"publisher","first-page":"163","DOI":"10.1159\/000077267","volume":"9","author":"K Yukawa","year":"2004","unstructured":"Yukawa, K., et al.: Effects of insertion depth of cochlear implant electrodes upon speech perception. Audiol. Neurotol. 9(3), 163\u2013172 (2004)","journal-title":"Audiol. Neurotol."},{"issue":"2","key":"24_CR3","doi-asserted-by":"publisher","first-page":"220","DOI":"10.1007\/s10162-007-0076-9","volume":"8","author":"O Stakhovskaya","year":"2007","unstructured":"Stakhovskaya, O., et al.: Frequency map for the human cochlear spiral ganglion: implications for cochlear implants. J. Assoc. Res. Otolaryngol. 8(2), 220 (2007)","journal-title":"J. Assoc. Res. Otolaryngol."},{"key":"24_CR4","unstructured":"Zuniga, M.G., et al.: Tip fold-over in cochlear implantation: case series. Otol. Neurotol. Official Publ. Am. Otological Soc. Amer. Neurotol. Soc. Eur. Acad. Otol. Neurotol. 38(2), 199 (2017)"},{"issue":"3","key":"24_CR5","doi-asserted-by":"publisher","first-page":"94","DOI":"10.1016\/j.joto.2019.01.002","volume":"14","author":"A Dhanasingh","year":"2019","unstructured":"Dhanasingh, A., Jolly, C.: Review on cochlear implant electrode array tip fold-over and scalar deviation. J. Otol. 14(3), 94\u2013100 (2019)","journal-title":"J. Otol."},{"issue":"4","key":"24_CR6","doi-asserted-by":"publisher","first-page":"206","DOI":"10.1080\/14670100.2020.1730066","volume":"21","author":"A Ishiyama","year":"2020","unstructured":"Ishiyama, A., Risi, F., Boyd, P.: Potential insertion complications with cochlear implant electrodes. Cochlear Implants Int. 21(4), 206\u2013219 (2020)","journal-title":"Cochlear Implants Int."},{"issue":"9","key":"24_CR7","doi-asserted-by":"publisher","first-page":"1666","DOI":"10.1097\/MAO.0b013e3182a09cc3","volume":"34","author":"F Dirr","year":"2013","unstructured":"Dirr, F., et al.: Value of routine plain x-ray position checks after cochlear implantation. Otol. Neurotol. 34(9), 1666\u20131669 (2013)","journal-title":"Otol. Neurotol."},{"issue":"1","key":"24_CR8","doi-asserted-by":"publisher","first-page":"e28","DOI":"10.1097\/MAO.0000000000001652","volume":"39","author":"JL McJunkin","year":"2018","unstructured":"McJunkin, J.L., Durakovic, N., Herzog, J., Buchman, C.A.: Early outcomes with a slim, modiolar cochlear implant electrode array. Otol. Neurotol. 39(1), e28\u2013e33 (2018)","journal-title":"Otol. Neurotol."},{"key":"24_CR9","doi-asserted-by":"publisher","first-page":"s38","DOI":"10.1016\/j.bjorl.2017.05.003","volume":"86","author":"O Garaycochea","year":"2020","unstructured":"Garaycochea, O., Manrique-Huarte, R., Manrique, M.: Intra-operative radiological diagnosis of a tip roll-over electrode array displacement using fluoroscopy, when electrophysiological testing is normal: the importance of both techniques in cochlear implant surgery. Braz. J. Otorhinolaryngol. 86, s38\u2013s40 (2020)","journal-title":"Braz. J. Otorhinolaryngol."},{"issue":"6","key":"24_CR10","doi-asserted-by":"publisher","first-page":"346","DOI":"10.1080\/14992020400050044","volume":"43","author":"LT Cohen","year":"2004","unstructured":"Cohen, L.T., Saunders, E., Richardson, L.M.: Spatial spread of neural excitation: comparison of compound action potential and forward-masking data in cochlear implant recipients. Int. J. Audiol. 43(6), 346\u2013355 (2004)","journal-title":"Int. J. Audiol."},{"issue":"7","key":"24_CR11","doi-asserted-by":"publisher","first-page":"1598","DOI":"10.1017\/S0263574716000333","volume":"35","author":"J Pile","year":"2017","unstructured":"Pile, J., Wanna, G.B., Simaan, N.: Robot-assisted perception augmentation for online detection of insertion failure during cochlear implant surgery. Robotica 35(7), 1598\u20131615 (2017)","journal-title":"Robotica"},{"issue":"9","key":"24_CR12","doi-asserted-by":"publisher","first-page":"1115","DOI":"10.1097\/MAO.0000000000001915","volume":"39","author":"J Gabrielpillai","year":"2018","unstructured":"Gabrielpillai, J., Burck, I., Baumann, U., St\u00f6ver, T., Helbig, S.: Incidence for tip foldover during cochlear implantation. Otol. Neurotol. 39(9), 1115\u20131121 (2018)","journal-title":"Otol. Neurotol."},{"key":"24_CR13","unstructured":"Ahn, B.B.: The compact 3D convolutional neural network for medical images. Standford University (2017)"},{"key":"24_CR14","doi-asserted-by":"crossref","unstructured":"Jin, T., Cui, H., Zeng, S., Wang, X.: Learning deep spatial lung features by 3d convolutional neural network for early cancer detection. In: 2017 International Conference on Digital Image Computing: Techniques and Applications (DICTA). IEEE, Piscataway (2017)","DOI":"10.1109\/DICTA.2017.8227454"},{"key":"24_CR15","doi-asserted-by":"crossref","unstructured":"Hara, K., Kataoka, H., Satoh, Y.: Learning spatio-temporal features with 3d residual networks for action recognition. In: Proceedings of the IEEE international Conference on Computer Vision workshops, pp. 3154\u20133160 (2017)","DOI":"10.1109\/ICCVW.2017.373"},{"key":"24_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101659","volume":"61","author":"D Zhang","year":"2020","unstructured":"Zhang, D., Wang, J., Noble, J.H., Dawant, B.M.: HeadLocNet: deep convolutional neural networks for accurate classification and multi-landmark localization of head CTs. Med. Image Anal. 61, 101659 (2020)","journal-title":"Med. Image Anal."},{"key":"24_CR17","doi-asserted-by":"crossref","unstructured":"Jnawali, K., Arbabshirani, M.R., Rao, N., Patel, A.A.: Deep 3D convolution neural network for CT brain hemorrhage classification. In: Medical Imaging 2018: Computer-Aided Diagnosis, vol. 10575, pp. 307\u2013313. SPIE (2018)","DOI":"10.1117\/12.2293725"},{"key":"24_CR18","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 \u2014 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"},{"issue":"9","key":"24_CR19","doi-asserted-by":"publisher","first-page":"2625","DOI":"10.1109\/TBME.2011.2160262","volume":"58","author":"JH Noble","year":"2011","unstructured":"Noble, J.H., Labadie, R.F., Majdani, O., Dawant, B.M.: Automatic segmentation of intra-cochlear anatomy in conventional CT. IEEE Trans. Biomed. Eng 58(9), 2625\u20132632 (2011)","journal-title":"IEEE Trans. Biomed. Eng"},{"issue":"4","key":"24_CR20","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1097\/MAO.0b013e31819e61ed","volume":"30","author":"JH Noble","year":"2009","unstructured":"Noble, J.H., Dawant, B.M., Warren, F.M., Labadie, R.F.: Automatic identification and 3D rendering of temporal bone anatomy. Otol. Neurotol. 30(4), 436\u2013442 (2009)","journal-title":"Otol. Neurotol."},{"key":"24_CR21","doi-asserted-by":"crossref","unstructured":"Noble, J.H., Warren, F.M., Labadie, R.F., Dawant, B.M.: Automatic segmentation of the facial nerve and chorda tympani using image registration and statistical priors. In: Medical Imaging 2008: Image Processing, vol. 6914, p. 69140P. International Society for Optics and Photonics (2008)","DOI":"10.1117\/12.772034"},{"key":"24_CR22","doi-asserted-by":"crossref","unstructured":"Khan, M.M., Banalagay, R., Labadie, R.F., Noble, J.H.: Sensitivity of intra-cochlear anatomy segmentation methods to varying image acquisition parameters. In: Medical Imaging 2022: Image-Guided Procedures, Robotic Interventions, and Modeling, vol. 12034, pp. 111\u2013116. SPIE (2022)","DOI":"10.1117\/12.2611684"},{"key":"24_CR23","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"121","DOI":"10.1007\/978-3-030-32226-7_14","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019","author":"Z Wang","year":"2019","unstructured":"Wang, Z., et al: Deep learning based metal artifacts reduction in post-operative cochlear implant CT imaging. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11769, pp. 121\u2013129. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32226-7_14"},{"key":"24_CR24","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"287","DOI":"10.1007\/978-3-319-75238-9_25","volume-title":"Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries","author":"F Isensee","year":"2018","unstructured":"Isensee, F., Kickingereder, P., Wick, W., Bendszus, M., Maier-Hein, K.H.: Brain tumor segmentation and radiomics survival prediction: Contribution to the brats 2017 challenge. In: Crimi, A., Bakas, S., Kuijf, H., Menze, B., Reyes, M. (eds.) BrainLes 2017. LNCS, vol. 10670, pp. 287\u2013297. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-75238-9_25"},{"key":"24_CR25","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"630","DOI":"10.1007\/978-3-319-46493-0_38","volume-title":"Computer Vision \u2013 ECCV 2016","author":"K He","year":"2016","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Identity mappings in deep residual networks. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9908, pp. 630\u2013645. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46493-0_38"},{"key":"24_CR26","unstructured":"Kayalibay, B., Jensen, G., van der Smagt, P.: CNN-based segmentation of medical imaging data (2017). arXiv preprintarXiv:1701.03056"},{"key":"24_CR27","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":"24_CR28","doi-asserted-by":"crossref","unstructured":"Tan, Q., Gao, L., Lai, Y.K., Xia, S.: Variational autoencoders for deforming 3d mesh models. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5841\u20135850 (2018)","DOI":"10.1109\/CVPR.2018.00612"},{"key":"24_CR29","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"274","DOI":"10.1007\/978-3-030-72084-1_25","volume-title":"Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries","author":"MD Cirillo","year":"2021","unstructured":"Cirillo, M.D., Abramian, D., Eklund, A.: Vox2Vox: 3D-GAN for brain tumour segmentation. In: Crimi, A., Bakas, S. (eds.) BrainLes 2020. LNCS, vol. 12658, pp. 274\u2013284. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-72084-1_25"},{"issue":"5","key":"24_CR30","doi-asserted-by":"publisher","first-page":"444","DOI":"10.1097\/01.moo.0000134452.24819.c0","volume":"12","author":"JT Rubinstein","year":"2004","unstructured":"Rubinstein, J.T.: How cochlear implants encode speech. Curr. Opin. Otolaryngol. Head Neck Surg. 12(5), 444\u2013448 (2004)","journal-title":"Curr. Opin. Otolaryngol. Head Neck Surg."},{"issue":"5","key":"24_CR31","doi-asserted-by":"publisher","first-page":"695","DOI":"10.1682\/JRRD.2007.10.0173","volume":"45","author":"BS Wilson","year":"2008","unstructured":"Wilson, B.S., Dorman, M.F.: Cochlear implants: current designs and future possibilities. J. Rehabil. Res. Dev. 45(5), 695\u2013730 (2008)","journal-title":"J. Rehabil. Res. Dev."}],"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\/978-3-031-43996-4_24","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,4]],"date-time":"2024-07-04T16:05:56Z","timestamp":1720109156000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-43996-4_24"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031439957","9783031439964"],"references-count":31,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-43996-4_24","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":"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)"}}]}}