{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T14:55:09Z","timestamp":1776956109360,"version":"3.51.4"},"reference-count":72,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2022,10,2]],"date-time":"2022-10-02T00:00:00Z","timestamp":1664668800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Monique Dornonville de la Cour foundation, Switzerland"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>Visual assessment based on intraoperative 2D X-rays remains the predominant aid for intraoperative decision-making, surgical guidance, and error prevention. However, correctly assessing the 3D shape of complex anatomies, such as the spine, based on planar fluoroscopic images remains a challenge even for experienced surgeons. This work proposes a novel deep learning-based method to intraoperatively estimate the 3D shape of patients\u2019 lumbar vertebrae directly from sparse, multi-view X-ray data. High-quality and accurate 3D reconstructions were achieved with a learned multi-view stereo machine approach capable of incorporating the X-ray calibration parameters in the neural network. This strategy allowed a priori knowledge of the spinal shape to be acquired while preserving patient specificity and achieving a higher accuracy compared to the state of the art. Our method was trained and evaluated on 17,420 fluoroscopy images that were digitally reconstructed from the public CTSpine1K dataset. As evaluated by unseen data, we achieved an 88% average F1 score and a 71% surface score. Furthermore, by utilizing the calibration parameters of the input X-rays, our method outperformed a counterpart method in the state of the art by 22% in terms of surface score. This increase in accuracy opens new possibilities for surgical navigation and intraoperative decision-making solely based on intraoperative data, especially in surgical applications where the acquisition of 3D image data is not part of the standard clinical workflow.<\/jats:p>","DOI":"10.3390\/jimaging8100271","type":"journal-article","created":{"date-parts":[[2022,10,8]],"date-time":"2022-10-08T23:39:31Z","timestamp":1665272371000},"page":"271","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":30,"title":["X23D\u2014Intraoperative 3D Lumbar Spine Shape Reconstruction Based on Sparse Multi-View X-ray Data"],"prefix":"10.3390","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6529-487X","authenticated-orcid":false,"given":"Sascha","family":"Jecklin","sequence":"first","affiliation":[{"name":"Research in Orthopedic Computer Science, Balgrist University Hospital, University of Zurich, 8008 Zurich, Switzerland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Carla","family":"Jancik","sequence":"additional","affiliation":[{"name":"Research in Orthopedic Computer Science, Balgrist University Hospital, University of Zurich, 8008 Zurich, Switzerland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mazda","family":"Farshad","sequence":"additional","affiliation":[{"name":"Department of Orthopedics, Balgrist University Hospital, University of Zurich, 8008 Zurich, Switzerland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Philipp","family":"F\u00fcrnstahl","sequence":"additional","affiliation":[{"name":"Research in Orthopedic Computer Science, Balgrist University Hospital, University of Zurich, 8008 Zurich, Switzerland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2572-1798","authenticated-orcid":false,"given":"Hooman","family":"Esfandiari","sequence":"additional","affiliation":[{"name":"Research in Orthopedic Computer Science, Balgrist University Hospital, University of Zurich, 8008 Zurich, Switzerland"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,10,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"E16","DOI":"10.3171\/2020.8.FOCUS20602","article-title":"Medical malpractice in spine surgery: A review","volume":"49","author":"Medress","year":"2020","journal-title":"Neurosurg. Focus FOC"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1625","DOI":"10.1016\/j.spinee.2018.02.003","article-title":"Risk factors for perioperative morbidity in spine surgeries of different complexities: A multivariate analysis of 1009 consecutive patients","volume":"18","author":"Farshad","year":"2018","journal-title":"Spine J."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1007\/s00586-011-2011-3","article-title":"Accuracy of pedicle screw placement: A systematic review of prospective in vivo studies comparing free hand, fluoroscopy guidance and navigation techniques","volume":"21","author":"Gelalis","year":"2012","journal-title":"Eur. Spine J."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Hu, Y.H., Niu, C.C., Hsieh, M.K., Tsai, T.T., Chen, W.J., and Lai, P.L. (2019). Cage positioning as a risk factor for posterior cage migration following transforaminal lumbar interbody fusion\u2014An analysis of 953 cases. BMC Musculoskelet. Disord., 20.","DOI":"10.1186\/s12891-019-2630-0"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"306","DOI":"10.3171\/2009.9.SPINE09261","article-title":"Accuracy of pedicle screw placement in the lumbosacral spine using conventional technique: Computed tomography postoperative assessment in 102 consecutive patients","volume":"12","author":"Amato","year":"2010","journal-title":"J. Neurosurg. Spine"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1097\/00007632-199001000-00004","article-title":"Accuracy of pedicular screw placement in vivo","volume":"15","author":"Gertzbein","year":"1990","journal-title":"Spine"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"402","DOI":"10.1007\/BF01834068","article-title":"Accuracy of pedicle screw insertion: A prospective CT study in 30 low back patients","volume":"6","author":"Laine","year":"1997","journal-title":"Eur. Spine J."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"E465","DOI":"10.1097\/BRS.0b013e3181d1021a","article-title":"Complications of pedicle screw fixation in scoliosis surgery: A systematic review","volume":"35","author":"Hicks","year":"2010","journal-title":"Spine"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"866","DOI":"10.1016\/j.wneu.2014.06.023","article-title":"Accuracy of pedicle screw placement in the thoracic and lumbosacral spine using a conventional intraoperative fluoroscopy-guided technique: A national neurosurgical education and training center analysis of 1236 consecutive screws","volume":"82","author":"Nevzati","year":"2014","journal-title":"World Neurosurg."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"2843","DOI":"10.1007\/s00586-017-5170-z","article-title":"Do position and size matter? An analysis of cage and placement variables for optimum lordosis in PLIF reconstruction","volume":"26","author":"Landham","year":"2017","journal-title":"Eur. Spine J."},{"key":"ref_11","first-page":"9142074","article-title":"Malposition of Cage in Minimally Invasive Oblique Lumbar Interbody Fusion","volume":"2018","author":"Kraiwattanapong","year":"2018","journal-title":"Case Rep. Orthop."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"767","DOI":"10.1097\/01.BRS.0000112071.69448.A1","article-title":"Assessment of pedicle screw placement utilizing conventional radiography and computed tomography: A proposed systematic approach to improve accuracy of interpretation","volume":"29","author":"Learch","year":"2004","journal-title":"Spine"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1249","DOI":"10.1097\/00007632-199706010-00016","article-title":"Reliability of roentgenogram evaluation of pedicle screw position","volume":"22","author":"Ferrick","year":"1997","journal-title":"Spine"},{"key":"ref_14","first-page":"547","article-title":"Stepwise methodology for plain radiographic assessment of pedicle screw placement: A comparison with computed tomography","volume":"19","author":"Choma","year":"2006","journal-title":"Clin. Spine Surg."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"196","DOI":"10.3171\/2013.11.SPINE13413","article-title":"The accuracy of pedicle screw placement using intraoperative image guidance systems: A systematic review","volume":"20","author":"Mason","year":"2014","journal-title":"J. Neurosurg. Spine"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"S19","DOI":"10.1016\/j.otsr.2019.05.021","article-title":"Role of 3D intraoperative imaging in orthopedic and trauma surgery","volume":"106","author":"Tonetti","year":"2020","journal-title":"Orthop. Traumatol. Surg. Res."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1016\/S1048-6666(00)80045-2","article-title":"Computer-assisted technology for spinal cage delivery","volume":"10","author":"Sati","year":"2000","journal-title":"Oper. Tech. Orthop."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"E4","DOI":"10.3171\/2020.5.FOCUS20353","article-title":"The feasibility of computer-assisted 3D navigation in multiple-level lateral lumbar interbody fusion in combination with posterior instrumentation for adult spinal deformity","volume":"49","author":"Strong","year":"2020","journal-title":"Neurosurg. Focus"},{"key":"ref_19","first-page":"1","article-title":"A review of computer-assisted orthopaedic surgery systems","volume":"16","author":"Wang","year":"2020","journal-title":"Int. J. Med. Robot. Comput. Assist. Surg."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"715","DOI":"10.1109\/42.736023","article-title":"Anatomy-based registration of CT-scan and intraoperative X-ray images for guiding a surgical robot","volume":"17","author":"Kazanzides","year":"1998","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Sundar, H., Khamene, A., Xu, C., Sauer, F., and Davatzikos, C. (2006, January 11\u201316). A novel 2D-3D registration algorithm for aligning fluoro images with 3D pre-op CT\/MR images. Proceedings of the Medical Imaging 2006: Visualization, Image-Guided Procedures, and Display, San Diego, CA, USA.","DOI":"10.1117\/12.654251"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1725","DOI":"10.1007\/s11548-019-02024-x","article-title":"A comparative analysis of intensity-based 2D\u20133D registration for intraoperative use in pedicle screw insertion surgeries","volume":"14","author":"Esfandiari","year":"2019","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1024","DOI":"10.1118\/1.1373400","article-title":"Validation of a two-to three-dimensional registration algorithm for aligning preoperative CT images and intraoperative fluoroscopy images","volume":"28","author":"Penney","year":"2001","journal-title":"Med. Phys."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1352","DOI":"10.1109\/TMI.2016.2521800","article-title":"A CNN regression approach for real-time 2D\/3D registration","volume":"35","author":"Miao","year":"2016","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1016\/j.media.2015.08.005","article-title":"Fully automated 2D\u20133D registration and verification","volume":"26","author":"Varnavas","year":"2015","journal-title":"Med. Image Anal."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"3025","DOI":"10.1038\/s41598-019-40057-z","article-title":"Risk factors for robot-assisted spinal pedicle screw malposition","volume":"9","author":"Zhang","year":"2019","journal-title":"Sci. Rep."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"162","DOI":"10.1016\/j.wneu.2012.03.011","article-title":"Worldwide survey on the use of navigation in spine surgery","volume":"79","author":"Lam","year":"2013","journal-title":"World Neurosurg."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"846","DOI":"10.1007\/s00586-010-1577-5","article-title":"Pedicle screw insertion accuracy with different assisted methods: A systematic review and meta-analysis of comparative studies","volume":"20","author":"Tian","year":"2011","journal-title":"Eur. Spine J."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1469","DOI":"10.1007\/s00586-009-1050-5","article-title":"Benefit and accuracy of intraoperative 3D-imaging after pedicle screw placement: A prospective study in stabilizing thoracolumbar fractures","volume":"18","author":"Beck","year":"2009","journal-title":"Eur. Spine J."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"2094","DOI":"10.1097\/BRS.0b013e318201129d","article-title":"Spinal Navigation: Standard Preoperative Versus Intraoperative Computed Tomography Data Set Acquisition for Computer-Guidance SystemRadiological and Clinical Study in 100 Consecutive Patients","volume":"36","author":"Costa","year":"2011","journal-title":"Spine"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"146","DOI":"10.1016\/j.jbiomech.2018.01.020","article-title":"Statistical shape modeling characterizes three-dimensional shape and alignment variability in the lumbar spine","volume":"69","author":"Hollenbeck","year":"2018","journal-title":"J. Biomech."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"2333","DOI":"10.1007\/s00586-021-06852-x","article-title":"Patient-specific statistical shape modeling for optimal spinal sagittal alignment in lumbar spinal fusion","volume":"30","author":"Furrer","year":"2021","journal-title":"Eur. Spine J."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"689","DOI":"10.1016\/j.patcog.2016.09.036","article-title":"Non-rigid free-form 2D\u20133D registration using a B-spline-based statistical deformation model","volume":"63","author":"Yu","year":"2017","journal-title":"Pattern Recognit."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"840","DOI":"10.1016\/j.media.2011.04.001","article-title":"2D\u20133D shape reconstruction of the distal femur from stereo X-ray imaging using statistical shape models","volume":"15","author":"Baka","year":"2011","journal-title":"Med. Image Anal."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"2796","DOI":"10.1109\/TMI.2019.2914400","article-title":"Toward automated 3D spine reconstruction from biplanar radiographs using CNN for statistical spine model fitting","volume":"38","author":"Aubert","year":"2019","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"880","DOI":"10.1038\/s41551-019-0466-4","article-title":"Patient-specific reconstruction of volumetric computed tomography images from a single projection view via deep learning","volume":"3","author":"Shen","year":"2019","journal-title":"Nat. Biomed. Eng."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Ying, X., Guo, H., Ma, K., Wu, J., Weng, Z., and Zheng, Y. (2019, January 16\u201320). X2CT-GAN: Reconstructing CT from biplanar X-rays with generative adversarial networks. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.01087"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Kasten, Y., Doktofsky, D., and Kovler, I. (2020, January 8). End-to-end convolutional neural network for 3D reconstruction of knee bones from bi-planar X-ray images. Proceedings of the International Workshop on Machine Learning for Medical Image Reconstruction, Lima, Peru.","DOI":"10.1007\/978-3-030-61598-7_12"},{"key":"ref_39","unstructured":"Li, R., Niu, K., Wu, D., and Vander Poorten, E. (2020, January 28\u201330). A Framework of Real-time Freehand Ultrasound Reconstruction based on Deep Learning for Spine Surgery. Proceedings of the 10th Conference on New Technologies for Computer and Robot Assisted Surgery, Barcelona, Spain."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., and Wells, W. (2016, January 17\u201321). Deformable 3D-2D Registration of Known Components for Image Guidance in Spine Surgery. Proceedings of the Medical Image Computing and Computer-Assisted Intervention\u2014MICCAI 2016, Athens, Greece.","DOI":"10.1007\/978-3-319-46723-8"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Dufour, P.A., Abdillahi, H., Ceklic, L., Wolf-Schnurrbusch, U., and Kowal, J. (2012, January 1\u20135). Pathology hinting as the combination of automatic segmentation with a statistical shape model. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Nice, France.","DOI":"10.1007\/978-3-642-33454-2_74"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Tatarchenko, M., Richter, S.R., Ranftl, R., Li, Z., Koltun, V., and Brox, T. (2019, January 15\u201320). What do single-view 3d reconstruction networks learn?. Proceedings of the Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00352"},{"key":"ref_43","first-page":"365","article-title":"Learning a multi-view stereo machine","volume":"2017","author":"Kar","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1157","DOI":"10.1007\/s11548-019-01973-7","article-title":"Pedicle screw navigation using surface digitization on the Microsoft HoloLens","volume":"14","author":"Liebmann","year":"2019","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"e0020","DOI":"10.2106\/JBJS.ST.19.00020","article-title":"Robotic-assisted pedicle screw placement during spine surgery","volume":"10","author":"Lieberman","year":"2020","journal-title":"JBJS Essent. Surg. Tech."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"695","DOI":"10.1007\/s11548-013-0957-9","article-title":"A low-cost tracked C-arm (TC-arm) upgrade system for versatile quantitative intraoperative imaging","volume":"9","author":"Amiri","year":"2014","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Chintalapani, G., Jain, A.K., Burkhardt, D.H., Prince, J.L., and Fichtinger, G. (2008, January 23\u201328). CTREC: C-arm tracking and reconstruction using elliptic curves. Proceedings of the 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, Anchorage, AK, USA.","DOI":"10.1109\/CVPRW.2008.4563029"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"361","DOI":"10.1117\/12.237798","article-title":"Dynamic geometrical calibration for 3D cerebral angiography","volume":"Volume 2708","author":"Navab","year":"1996","journal-title":"Proceedings of the Medical Imaging 1996: Physics of Medical Imaging"},{"key":"ref_49","unstructured":"Esfandiari, H., Martinez, J.F., Alvarez, A.G., Guy, P., Street, J., Anglin, C., and Hodgson, A.J. (2017, January 20\u201324). An automated, robust and closed form mini-RSA system for intraoperative C-Arm calibration. Proceedings of the CARS 2017\u2014Computer Assisted Radiology and Surgery, Barcelona, Spain."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1095","DOI":"10.1007\/s11548-020-02204-0","article-title":"Toward automatic C-arm positioning for standard projections in orthopedic surgery","volume":"15","author":"Kausch","year":"2020","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"key":"ref_51","first-page":"69","article-title":"A Deep Learning Approach for Single Shot C-Arm Pose Estimation","volume":"4","author":"Esfandiari","year":"2020","journal-title":"CAOS"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1016\/j.cmpb.2018.01.006","article-title":"Fully automatic cervical vertebrae segmentation framework for X-ray images","volume":"157","author":"Knapp","year":"2018","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Sa, R., Owens, W., Wiegand, R., and Chaudhary, V. (2016, January 16\u201320). Fast scale-invariant lateral lumbar vertebrae detection and segmentation in X-ray images. Proceedings of the 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Orlando, FL, USA.","DOI":"10.1109\/EMBC.2016.7590884"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"102115","DOI":"10.1016\/j.media.2021.102115","article-title":"Evaluation and comparison of accurate automated spinal curvature estimation algorithms with spinal anterior-posterior X-ray images: The AASCE2019 challenge","volume":"72","author":"Wang","year":"2021","journal-title":"Med. Image Anal."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"5485","DOI":"10.1088\/0031-9155\/57\/17\/5485","article-title":"Automatic localization of vertebral levels in x-ray fluoroscopy using 3D-2D registration: A tool to reduce wrong-site surgery","volume":"57","author":"Otake","year":"2012","journal-title":"Phys. Med. Biol."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Mushtaq, M., Akram, M.U., Alghamdi, N.S., Fatima, J., and Masood, R.F. (2022). Localization and Edge-Based Segmentation of Lumbar Spine Vertebrae to Identify the Deformities Using Deep Learning Models. Sensors, 22.","DOI":"10.3390\/s22041547"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"105833","DOI":"10.1016\/j.cmpb.2020.105833","article-title":"Automatic detection and segmentation of lumbar vertebrae from X-ray images for compression fracture evaluation","volume":"200","author":"Kim","year":"2021","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Navab, N., Hornegger, J., Wells, W.M., and Frangi, A.F. (2015, January 5\u20139). U-Net: Convolutional Networks for Biomedical Image Segmentation. Proceedings of the Medical Image Computing and Computer-Assisted Intervention\u2014MICCAI 2015, Munich, Germany.","DOI":"10.1007\/978-3-319-24553-9"},{"key":"ref_59","unstructured":"Deng, Y., Wang, C., Hui, Y., Li, Q., Li, J., Luo, S., Sun, M., Quan, Q., Yang, S., and Hao, Y. (2021). CTSpine1K: A Large-Scale Dataset for Spinal Vertebrae Segmentation in Computed Tomography. arXiv."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"789","DOI":"10.1016\/0360-3016(83)90003-2","article-title":"Multi-dimensional treatment planning: II. Beam\u2019s eye-view, back projection, and projection through CT sections","volume":"9","author":"Goitein","year":"1983","journal-title":"Int. J. Radiat. Oncol. Biol. Phys."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Doll\u00e1r, P., and Zitnick, C.L. (2014, January September). Microsoft coco: Common objects in context. Proceedings of the European Conference on Computer Vision, Zurich, Switzerland.","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3072959.3073599","article-title":"Tanks and temples: Benchmarking large-scale scene reconstruction","volume":"36","author":"Knapitsch","year":"2017","journal-title":"ACM Trans. Graph."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"2919","DOI":"10.1007\/s11263-020-01347-6","article-title":"Pix2Vox++: Multi-scale context-aware 3D object reconstruction from single and multiple images","volume":"128","author":"Xie","year":"2020","journal-title":"Int. J. Comput. Vis."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"13","DOI":"10.3389\/fninf.2014.00013","article-title":"ITK: Enabling reproducible research and open science","volume":"8","author":"McCormick","year":"2014","journal-title":"Front. Neuroinformatics"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"45","DOI":"10.3389\/fninf.2013.00045","article-title":"The Design of SimpleITK","volume":"7","author":"Lowekamp","year":"2013","journal-title":"Front. Neuroinformatics"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"106236","DOI":"10.1016\/j.cmpb.2021.106236","article-title":"TorchIO: A Python library for efficient loading, preprocessing, augmentation and patch-based sampling of medical images in deep learning","volume":"208","author":"Sparks","year":"2021","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_67","unstructured":"Wallach, H., Larochelle, H., Beygelzimer, A., d\u2019Alch\u00e9-Buc, F., Fox, E., and Garnett, R. (2019). PyTorch: An Imperative Style, High-Performance Deep Learning Library. Advances in Neural Information Processing Systems 32, Curran Associates, Inc."},{"key":"ref_68","unstructured":"Falcon, W., Borovec, J., W\u00e4lchli, A., Eggert, N., Schock, J., Jordan, J., Skafte, N., Bereznyuk, V., Harris, E., and Murrell, T. (Zenodo, 2020). Pytorchlightning\/pytorch-lightning: 0.7.6 Release, Zenodo."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1038\/s41592-019-0686-2","article-title":"SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python","volume":"17","author":"Virtanen","year":"2020","journal-title":"Nat. Methods"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1051\/sicotj\/2017008","article-title":"Orthopedic surgeons\u2019 knowledge regarding risk of radiation exposition: A survey analysis","volume":"3","author":"Kuyucu","year":"2017","journal-title":"SICOT-J"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"890","DOI":"10.1259\/bjr\/30125639","article-title":"A method to produce and validate a digitally reconstructed radiograph-based computer simulation for optimisation of chest radiographs acquired with a computed radiography imaging system","volume":"84","author":"Moore","year":"2011","journal-title":"Br. J. Radiol."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"15249","DOI":"10.1038\/s41598-021-94634-2","article-title":"2D\u20133D reconstruction of distal forearm bone from actual X-ray images of the wrist using convolutional neural networks","volume":"11","author":"Shiode","year":"2021","journal-title":"Sci. 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