{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,16]],"date-time":"2026-03-16T23:14:48Z","timestamp":1773702888784,"version":"3.50.1"},"publisher-location":"Cham","reference-count":15,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030009366","type":"print"},{"value":"9783030009373","type":"electronic"}],"license":[{"start":{"date-parts":[[2018,1,1]],"date-time":"2018-01-01T00:00:00Z","timestamp":1514764800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2018,1,1]],"date-time":"2018-01-01T00:00:00Z","timestamp":1514764800000},"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":[[2018]]},"DOI":"10.1007\/978-3-030-00937-3_12","type":"book-chapter","created":{"date-parts":[[2018,9,12]],"date-time":"2018-09-12T23:26:08Z","timestamp":1536794768000},"page":"98-106","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":101,"title":["DeepDRR \u2013 A Catalyst for Machine Learning in Fluoroscopy-Guided Procedures"],"prefix":"10.1007","author":[{"given":"Mathias","family":"Unberath","sequence":"first","affiliation":[]},{"given":"Jan-Nico","family":"Zaech","sequence":"additional","affiliation":[]},{"given":"Sing Chun","family":"Lee","sequence":"additional","affiliation":[]},{"given":"Bastian","family":"Bier","sequence":"additional","affiliation":[]},{"given":"Javad","family":"Fotouhi","sequence":"additional","affiliation":[]},{"given":"Mehran","family":"Armand","sequence":"additional","affiliation":[]},{"given":"Nassir","family":"Navab","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2018,9,13]]},"reference":[{"key":"12_CR1","doi-asserted-by":"crossref","unstructured":"Kooi, T., et al.: Large scale deep learning for computer aided detection of mammographic lesions. Med. Image Anal. 35, 303\u2013312 (2017)","DOI":"10.1016\/j.media.2016.07.007"},{"key":"12_CR2","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"231","DOI":"10.1007\/978-3-319-66179-7_27","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2017","author":"AG Roy","year":"2017","unstructured":"Roy, A.G., Conjeti, S., Sheet, D., Katouzian, A., Navab, N., Wachinger, C.: Error corrective boosting for learning fully convolutional networks with limited data. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10435, pp. 231\u2013239. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-66179-7_27"},{"key":"12_CR3","doi-asserted-by":"crossref","unstructured":"Milletari, F., Navab, N., Ahmadi, S.A.: V-Net: fully convolutional neural networks for volumetric medical image segmentation. In: 2016 Fourth International Conference on 3D Vision (3DV), pp. 565\u2013571. IEEE (2016)","DOI":"10.1109\/3DV.2016.79"},{"key":"12_CR4","doi-asserted-by":"crossref","unstructured":"Li, Y., Liang, W., Zhang, Y., An, H., Tan, J.: Automatic lumbar vertebrae detection based on feature fusion deep learning for partial occluded C-arm X-ray images. In: 2016 IEEE 38th Annual International Conference of the Engineering in Medicine and Biology Society (EMBC), pp. 647\u2013650. IEEE (2016)","DOI":"10.1109\/EMBC.2016.7590785"},{"key":"12_CR5","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1007\/s12194-017-0435-0","volume":"11","author":"T Terunuma","year":"2017","unstructured":"Terunuma, T., Tokui, A., Sakae, T.: Novel real-time tumor-contouring method using deep learning to prevent mistracking in X-ray fluoroscopy. Radiol. Phys. Technol. 11, 43\u201353 (2017)","journal-title":"Radiol. Phys. Technol."},{"key":"12_CR6","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"577","DOI":"10.1007\/978-3-319-66185-8_65","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2017","author":"P Ambrosini","year":"2017","unstructured":"Ambrosini, P., Ruijters, D., Niessen, W.J., Moelker, A., van Walsum, T.: Fully automatic and real-time catheter segmentation in X-ray fluoroscopy. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10434, pp. 577\u2013585. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-66185-8_65"},{"key":"12_CR7","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"453","DOI":"10.1007\/978-3-319-66179-7_52","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2017","author":"H Ma","year":"2017","unstructured":"Ma, H., Ambrosini, P., van Walsum, T.: Fast prospective detection of contrast inflow in X-ray angiograms with convolutional neural network and recurrent neural network. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10435, pp. 453\u2013461. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-66179-7_52"},{"issue":"11","key":"12_CR8","doi-asserted-by":"publisher","first-page":"1441","DOI":"10.1109\/TMI.2005.856749","volume":"24","author":"DB Russakoff","year":"2005","unstructured":"Russakoff, D.B., et al.: Fast generation of digitally reconstructed radiographs using attenuation fields with application to 2D\u20133D image registration. IEEE Trans. Med. Imaging 24(11), 1441\u20131454 (2005)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"12_CR9","doi-asserted-by":"crossref","unstructured":"Bier, B., et al.: X-ray-transform invariant anatomical landmark detection for pelvic trauma surgery. In: Frangi, A.F., et al. (eds.) MICCAI 2018. LNCS, vol. 11073, pp. 55\u201363. Springer, Heidelberg (2018)","DOI":"10.1007\/978-3-030-00937-3_7"},{"key":"12_CR10","doi-asserted-by":"crossref","unstructured":"Hubbell, J.H., Seltzer, S.M.: Tables of X-ray mass attenuation coefficients and mass energy-absorption coefficients 1 keV to 20 MeV for elements Z\u00a0=\u00a01 to 92 and 48 additional substances of dosimetric interest. Technical report, National Institute of Standards and Technology (1995)","DOI":"10.6028\/NIST.IR.5632"},{"issue":"11","key":"12_CR11","doi-asserted-by":"publisher","first-page":"4878","DOI":"10.1118\/1.3231824","volume":"36","author":"A Badal","year":"2009","unstructured":"Badal, A., Badano, A.: Accelerating Monte Carlo simulations of photon transport in a voxelized geometry using a massively parallel graphics processing unit. Med. Phys. 36(11), 4878\u20134880 (2009)","journal-title":"Med. Phys."},{"issue":"2","key":"12_CR12","doi-asserted-by":"publisher","first-page":"459","DOI":"10.1088\/0031-9155\/45\/2\/314","volume":"45","author":"W Schneider","year":"2000","unstructured":"Schneider, W., Bortfeld, T., Schlegel, W.: Correlation between CT numbers and tissue parameters needed for Monte Carlo simulations of clinical dose distributions. Phys. Med. Biol. 45(2), 459 (2000)","journal-title":"Phys. Med. Biol."},{"key":"12_CR13","doi-asserted-by":"crossref","unstructured":"Sisniega, A., et al.: Monte carlo study of the effects of system geometry and antiscatter grids on cone-beam CT scatter distributions. Med. Phys. 40(5) (2013)","DOI":"10.1118\/1.4801895"},{"key":"12_CR14","doi-asserted-by":"crossref","unstructured":"Maier, J., Sawall, S., Kachelrie\u00df, M.: Deep scatter estimation (DSE): feasibility of using a deep convolutional neural network for real-time X-ray scatter prediction in cone-beam CT. In: SPIE Medical Imaging, SPIE (2018)","DOI":"10.1117\/12.2292919"},{"key":"12_CR15","doi-asserted-by":"crossref","unstructured":"Zhang, H., Ouyang, L., Ma, J., Huang, J., Chen, W., Wang, J.: Noise correlation in CBCT projection data and its application for noise reduction in low-dose CBCT. Med. Phys. 41(3) (2014)","DOI":"10.1118\/1.4865782"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2018"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-00937-3_12","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,13]],"date-time":"2023-09-13T00:19:37Z","timestamp":1694564377000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-00937-3_12"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018]]},"ISBN":["9783030009366","9783030009373"],"references-count":15,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-00937-3_12","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018]]},"assertion":[{"value":"13 September 2018","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":"Granada","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Spain","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2018","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16 September 2018","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20 September 2018","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2018","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.miccai2018.org\/en\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}