{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,19]],"date-time":"2026-06-19T20:44:53Z","timestamp":1781901893238,"version":"3.54.5"},"reference-count":20,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2022,3,11]],"date-time":"2022-03-11T00:00:00Z","timestamp":1646956800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,3,11]],"date-time":"2022-03-11T00:00:00Z","timestamp":1646956800000},"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":["Int J CARS"],"published-print":{"date-parts":[[2022,5]]},"DOI":"10.1007\/s11548-022-02585-4","type":"journal-article","created":{"date-parts":[[2022,3,11]],"date-time":"2022-03-11T17:02:55Z","timestamp":1647018175000},"page":"937-943","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Generation of synthetic training data for SEEG electrodes segmentation"],"prefix":"10.1007","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2204-5499","authenticated-orcid":false,"given":"Anja","family":"Pantovic","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoxi","family":"Ren","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"C\u00e9dric","family":"Wemmert","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ir\u00e8ne","family":"Ollivier","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Caroline","family":"Essert","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,3,11]]},"reference":[{"issue":"4","key":"2585_CR1","doi-asserted-by":"publisher","first-page":"512","DOI":"10.1111\/epi.13709","volume":"58","author":"IE Scheffer","year":"2017","unstructured":"Scheffer IE, Berkovic S, Capovilla G, Connolly MB, French J, Guilhoto L, Hirsch E, Jain S, Mathern GW, Mosh\u00e9 SL, Nordli DR, Perucca E, Tomson T, Wiebe S, Zhang Y-H, Zuberi SM (2017) ILAE classification of the epilepsies: position paper of the ILAE commission for classification and terminology. Epilepsia 58(4):512\u2013521","journal-title":"Epilepsia"},{"key":"2585_CR2","doi-asserted-by":"crossref","unstructured":"Talairach J, Bancaud J (1966) Lesion,\u201c irritative \u201d zone and epileptogenic focus. Stereo Funct Neurosurg 27(1\u20133):91\u201394","DOI":"10.1159\/000103937"},{"issue":"1","key":"2585_CR3","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1016\/j.neucli.2017.11.006","volume":"48","author":"L Minotti","year":"2018","unstructured":"Minotti L, Montavont A, Scholly J, Tyvaert L, Taussig D (2018) Indications and limits of stereoelectroencephalography (SEEG). Neurophysiol Clinique 48(1):15\u201324","journal-title":"Neurophysiol Clinique"},{"key":"2585_CR4","doi-asserted-by":"crossref","unstructured":"Meesters S, Ossenblok P, Colon A, Schijns O, Florack L, Boon P, Wagner L, Fuster A (2015) Automated identification of intracranial depth electrodes in computed tomography data. In: IEEE 12th international symposium on biomedical imaging (ISBI), pp 976\u2013979","DOI":"10.1109\/ISBI.2015.7164034"},{"key":"2585_CR5","doi-asserted-by":"publisher","first-page":"124","DOI":"10.1186\/s12859-017-1545-8","volume":"18","author":"M Narizzano","year":"2017","unstructured":"Narizzano M, Arnulfo G, Ricci S, Toselli B, Tisdall M, Canessa A, Fato M, Cardinale F (2017) SEEG assistant: a 3DSlicer extension to support epilepsy surgery. BMC Bioinf 18:124","journal-title":"BMC Bioinf"},{"key":"2585_CR6","doi-asserted-by":"crossref","unstructured":"Granados A, Vakharia V, Rodionov R, Schweiger M, Vos S, O\u2019Keeffe A, Li K, Wu C, Miserocchi A, Mcevoy A, Clarkson M, Duncan J, Sparks R, Ourselin S (2018) Automatic segmentation of stereoelectroencephalography (SEEG) electrodes post-implantation considering bending. Int J Comput Assist Radiol Surg 13:935\u2013946","DOI":"10.1007\/s11548-018-1740-8"},{"issue":"5","key":"2585_CR7","doi-asserted-by":"publisher","first-page":"215","DOI":"10.1049\/htl.2018.5070","volume":"5","author":"S Benadi","year":"2018","unstructured":"Benadi S, Ollivier I, Essert C (2018) Comparison of interactive and automatic segmentation of stereoelectroencephalography electrodes on computed tomography post-operative images: preliminary results. Healthcare Technol Lett 5(5):215\u2013220","journal-title":"Healthcare Technol Lett"},{"key":"2585_CR8","doi-asserted-by":"crossref","unstructured":"Pantovic A, Ollivier I, Essert C (2022) 2D and 3D-UNet for segmentation of SEEG electrode contacts on post-operative CT scans. In: Proceedings of medical imaging: visualization and image-guided procedures (to appear)","DOI":"10.1117\/12.2606538"},{"issue":"10162","key":"2585_CR9","doi-asserted-by":"publisher","first-page":"2388","DOI":"10.1016\/S0140-6736(18)31645-3","volume":"392","author":"S Chilamkurthy","year":"2018","unstructured":"Chilamkurthy S, Ghosh R, Tanamala S, Biviji M, Campeau NG, Venugopal VK, Mahajan V, Rao P, Warier P (2018) Deep learning algorithms for detection of critical findings in head CT scans: a retrospective study. Lancet 392(10162):2388\u20132396","journal-title":"Lancet"},{"issue":"3","key":"2585_CR10","doi-asserted-by":"publisher","first-page":"386","DOI":"10.1111\/epi.13298","volume":"57","author":"JP Mullin","year":"2016","unstructured":"Mullin JP, Shriver M, Alomar S, Najm I, Bulacio J, Chauvel P, Gonzalez-Martinez J (2016) Is SEEG safe? A systematic review and meta-analysis of stereoelectro-encephalography-related complications. Epilepsia 57(3):386\u2013401","journal-title":"Epilepsia"},{"key":"2585_CR11","doi-asserted-by":"crossref","unstructured":"Kikinis R, Pieper SD, Vosburgh KG (2014) 3D Slicer:A platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, New York, pp 277\u2013289","DOI":"10.1007\/978-1-4614-7657-3_19"},{"key":"2585_CR12","doi-asserted-by":"crossref","unstructured":"Suzuki S, be K (1985) Topological structural analysis of digitized binary images by border following. Comput Vis Graphics Image Process 30(1):32\u201346","DOI":"10.1016\/0734-189X(85)90016-7"},{"issue":"2","key":"2585_CR13","doi-asserted-by":"publisher","first-page":"229","DOI":"10.2217\/iim.12.13","volume":"4","author":"FE Boas","year":"2012","unstructured":"Boas FE, Fleischmann D (2012) CT artifacts: causes and reduction techniques. Imag Med 4(2):229\u2013240","journal-title":"Imag Med"},{"key":"2585_CR14","doi-asserted-by":"crossref","unstructured":"Mehrania, A, Ay M, Rahmim A, Zaidi H (2011) Sparsity constrained sinogram inpainting for metal artifact reduction in x-ray computed tomography. In: IEEE symposium on nuclear science, pp 3694\u20133699","DOI":"10.1109\/NSSMIC.2011.6153697"},{"issue":"6","key":"2585_CR15","doi-asserted-by":"publisher","first-page":"1370","DOI":"10.1109\/TMI.2018.2823083","volume":"37","author":"Y Zhang","year":"2018","unstructured":"Zhang Y, Yu H (2018) Convolutional neural network based metal artifact reduction in X-ray computed tomography. IEEE Trans Med Imag 37(6):1370\u20131381","journal-title":"IEEE Trans Med Imag"},{"issue":"1","key":"2585_CR16","doi-asserted-by":"publisher","first-page":"228","DOI":"10.1109\/TMI.2020.3025064","volume":"40","author":"L Yu","year":"2020","unstructured":"Yu L, Zhang Z, Li X, Xing L (2020) Deep sinogram completion with image prior for metal artifact reduction in CT images. IEEE Trans Med Imag 40(1):228\u2013238","journal-title":"IEEE Trans Med Imag"},{"key":"2585_CR17","doi-asserted-by":"crossref","unstructured":"Megherbi N, Breckon T.P, Flitton G.T, Mouton A (2013) Radon transform based automatic metal artefacts generation for 3D threat image projection. In: Optics and photonics for counterterrorism, crime fighting and defence IX; and optical materials and biomaterials in security and defence systems technology X, vol 8901. International Society for Optics and Photonics, SPIE, pp 94\u2013102","DOI":"10.1117\/12.2028506"},{"issue":"3","key":"2585_CR18","doi-asserted-by":"publisher","first-page":"691","DOI":"10.1109\/23.775600","volume":"46","author":"B De Man","year":"1999","unstructured":"De Man B, Nuyts J, Dupont P, Marchal G, Suetens P (1999) Metal streak artifacts in x-ray computed tomography: a simulation study. IEEE Trans Nucl Sci 46(3):691\u2013696","journal-title":"IEEE Trans Nucl Sci"},{"key":"2585_CR19","unstructured":"Palenstijn WJ, Batenburg KJ, Sijbers J (2013) The astra tomography toolbox. In: 13th International conference on computational and mathematical methods in science and engineering, pp 1139\u20131145"},{"key":"2585_CR20","doi-asserted-by":"crossref","unstructured":"Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: Medical image computing and computer-assisted intervention. Springer, pp 234\u2013241","DOI":"10.1007\/978-3-319-24574-4_28"}],"container-title":["International Journal of Computer Assisted Radiology and Surgery"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11548-022-02585-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11548-022-02585-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11548-022-02585-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,16]],"date-time":"2022-05-16T11:34:08Z","timestamp":1652700848000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11548-022-02585-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,3,11]]},"references-count":20,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2022,5]]}},"alternative-id":["2585"],"URL":"https:\/\/doi.org\/10.1007\/s11548-022-02585-4","relation":{},"ISSN":["1861-6429"],"issn-type":[{"value":"1861-6429","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,3,11]]},"assertion":[{"value":"18 January 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 February 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 March 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no conflict of interest to declare.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"This research study was conducted retrospectively from anonymised data, in accordance with the ethical standards of our institution and with the 1964 Declaration of Helsinki and its later amendments or comparable ethical standards.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval"}},{"value":"Informed consent was obtained from all individual participants included in the study.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}}]}}