{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T16:11:39Z","timestamp":1774627899258,"version":"3.50.1"},"reference-count":37,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2021,8,27]],"date-time":"2021-08-27T00:00:00Z","timestamp":1630022400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>Computer aided orthopedic surgery suffers from low clinical adoption, despite increased accuracy and patient safety. This can partly be attributed to cumbersome and often radiation intensive registration methods. Emerging RGB-D sensors combined with artificial intelligence data-driven methods have the potential to streamline these procedures. However, developing such methods requires vast amount of data. To this end, a multi-modal approach that enables acquisition of large clinical data, tailored to pedicle screw placement, using RGB-D sensors and a co-calibrated high-end optical tracking system was developed. The resulting dataset comprises RGB-D recordings of pedicle screw placement along with individually tracked ground truth poses and shapes of spine levels L1\u2013L5 from ten cadaveric specimens. Besides a detailed description of our setup, quantitative and qualitative outcome measures are provided. We found a mean target registration error of 1.5 mm. The median deviation between measured and ground truth bone surface was 2.4 mm. In addition, a surgeon rated the overall alignment based on 10% random samples as 5.8 on a scale from 1 to 6. Generation of labeled RGB-D data for orthopedic interventions with satisfactory accuracy is feasible, and its publication shall promote future development of data-driven artificial intelligence methods for fast and reliable intraoperative registration.<\/jats:p>","DOI":"10.3390\/jimaging7090164","type":"journal-article","created":{"date-parts":[[2021,8,27]],"date-time":"2021-08-27T09:53:23Z","timestamp":1630058003000},"page":"164","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["SpineDepth: A Multi-Modal Data Collection Approach for Automatic Labelling and Intraoperative Spinal Shape Reconstruction Based on RGB-D Data"],"prefix":"10.3390","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7722-2267","authenticated-orcid":false,"given":"Florentin","family":"Liebmann","sequence":"first","affiliation":[{"name":"Research in Orthopedic Computer Science, Balgrist University Hospital, University of Zurich, 8008 Zurich, Switzerland"},{"name":"Laboratory for Orthopaedic Biomechanics, ETH Zurich, 8093 Zurich, Switzerland"}]},{"given":"Dominik","family":"St\u00fctz","sequence":"additional","affiliation":[{"name":"Research in Orthopedic Computer Science, Balgrist University Hospital, University of Zurich, 8008 Zurich, Switzerland"},{"name":"Computer Vision and Geometry Group, ETH Zurich, 8093 Zurich, Switzerland"}]},{"given":"Daniel","family":"Suter","sequence":"additional","affiliation":[{"name":"Research in Orthopedic Computer Science, Balgrist University Hospital, University of Zurich, 8008 Zurich, Switzerland"},{"name":"Department of Orthopedics, Balgrist University Hospital, University of Zurich, 8008 Zurich, Switzerland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6529-487X","authenticated-orcid":false,"given":"Sascha","family":"Jecklin","sequence":"additional","affiliation":[{"name":"Research in Orthopedic Computer Science, Balgrist University Hospital, University of Zurich, 8008 Zurich, Switzerland"}]},{"given":"Jess G.","family":"Snedeker","sequence":"additional","affiliation":[{"name":"Laboratory for Orthopaedic Biomechanics, ETH Zurich, 8093 Zurich, Switzerland"},{"name":"Department of Orthopedics, Balgrist University Hospital, University of Zurich, 8008 Zurich, Switzerland"}]},{"given":"Mazda","family":"Farshad","sequence":"additional","affiliation":[{"name":"Department of Orthopedics, Balgrist University Hospital, University of Zurich, 8008 Zurich, Switzerland"}]},{"given":"Philipp","family":"F\u00fcrnstahl","sequence":"additional","affiliation":[{"name":"Research in Orthopedic Computer Science, Balgrist University Hospital, University of Zurich, 8008 Zurich, Switzerland"}]},{"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"}]}],"member":"1968","published-online":{"date-parts":[[2021,8,27]]},"reference":[{"key":"ref_1","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 studies comparing free hand, fluoroscopy guidance and navigation techniques","volume":"21","author":"Gelalis","year":"2012","journal-title":"Eur. Spine J."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"664","DOI":"10.1016\/j.wneu.2019.02.217","article-title":"Accuracy of current techniques for placement of pedicle screws in the spine: A comprehensive systematic review and meta-analysis of 51,161 screws","volume":"126","author":"Ishida","year":"2019","journal-title":"World Neurosurg."},{"key":"ref_3","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_4","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s13167-017-0084-8","article-title":"Computer-aided surgery meets predictive, preventive, and personalized medicine","volume":"8","author":"Joskowicz","year":"2017","journal-title":"EPMA J."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1016\/j.media.2016.06.036","article-title":"Computer Aided Orthopaedic Surgery: Incremental shift or paradigm change?","volume":"100","author":"Joskowicz","year":"2016","journal-title":"Med. Image Anal."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Picard, F., Clarke, J., Deep, K., and Gregori, A. (2014). Computer assisted knee replacement surgery: Is the movement mainstream?. Orthop. Muscular Syst., 3.","DOI":"10.4172\/2161-0533.1000153"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"642","DOI":"10.1016\/j.media.2010.03.005","article-title":"A review of 3D\/2D registration methods for image-guided interventions","volume":"16","author":"Markelj","year":"2012","journal-title":"Med. Image Anal."},{"key":"ref_8","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_9","doi-asserted-by":"crossref","first-page":"226","DOI":"10.1049\/htl.2019.0078","article-title":"Deep segmentation leverages geometric pose estimation in computer-aided total knee arthroplasty","volume":"6","author":"Rodrigues","year":"2019","journal-title":"Healthc. Technol. Lett."},{"key":"ref_10","first-page":"271","article-title":"Towards markerless computer-aided surgery combining deep segmentation and geometric pose estimation: Application in total knee arthroplasty","volume":"9","author":"Raposo","year":"2020","journal-title":"Comput. Methods Biomech. Biomed. Eng. Imaging Visualiz."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"967","DOI":"10.1007\/s11548-016-1396-1","article-title":"Calibration of RGBD camera and cone-beam CT for 3D intra-operative mixed reality visualization","volume":"11","author":"Lee","year":"2016","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"168","DOI":"10.1049\/htl.2017.0066","article-title":"Multi-modal imaging, model-based tracking, and mixed reality visualisation for orthopaedic surgery","volume":"4","author":"Lee","year":"2017","journal-title":"Healthc. Technol. Lett."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1080\/21681163.2020.1835556","article-title":"Feasibility of image-based augmented reality guidance of total shoulder arthroplasty using microsoft HoloLens 1","volume":"9","author":"Gu","year":"2020","journal-title":"Comput. Methods Biomech. Biomed. Eng. Imaging Visualiz."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"41","DOI":"10.3389\/fsurg.2021.640554","article-title":"Machine Vision Navigation in Spine Surgery","volume":"8","author":"Kalfas","year":"2021","journal-title":"Front. Surg."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Wadhwa, H., Malacon, K., Medress, Z.A., Leung, C., Sklar, M., and Zygourakis, C.C. (2021). First reported use of real-time intraoperative computed tomography angiography image registration using the Machine-vision Image Guided Surgery system: Illustrative case. J. Neurosurg. Case Lessons, 1.","DOI":"10.3171\/CASE2125"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Cabrera, E.V., Ortiz, L.E., da Silva, B.M., Clua, E.W., and Gon\u00e7alves, L.M. (2018). A versatile method for depth data error estimation in RGB-D sensors. Sensors, 18.","DOI":"10.3390\/s18093122"},{"key":"ref_17","unstructured":"Pratusevich, M., Chrisos, J., and Aditya, S. (2019). Quantitative Depth Quality Assessment of RGBD Cameras At Close Range Using 3D Printed Fixtures. arXiv."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Bajzik, J., Koniar, D., Hargas, L., Volak, J., and Janisova, S. (2020, January 25\u201328). Depth Sensor Selection for Specific Application. Proceedings of the 2020 ELEKTRO, Taormina, Italy.","DOI":"10.1109\/ELEKTRO49696.2020.9130293"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"12049","DOI":"10.1109\/JSEN.2020.2968477","article-title":"Single View 3D Reconstruction Based on Improved RGB-D Image","volume":"20","author":"Cao","year":"2020","journal-title":"IEEE Sens. J."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3072959.3073596","article-title":"Vnect: Real-time 3d human pose estimation with a single rgb camera","volume":"36","author":"Mehta","year":"2017","journal-title":"ACM Trans. Graph. (TOG)"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Schwarz, M., Schulz, H., and Behnke, S. (2015, January 26\u201330). RGB-D object recognition and pose estimation based on pre-trained convolutional neural network features. Proceedings of the 2015 IEEE International Conference on Robotics and Automation (ICRA), Seattle, WA, USA.","DOI":"10.1109\/ICRA.2015.7139363"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Hou, J., Dai, A., and Nie\u00dfner, M. (2019, January 15\u201320). 3d-sis: 3d semantic instance segmentation of rgb-d scans. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00455"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Azinovi\u0107, D., Martin-Brualla, R., Goldman, D.B., Nie\u00dfner, M., and Thies, J. (2021). Neural RGB-D Surface Reconstruction. arXiv.","DOI":"10.1109\/CVPR52688.2022.00619"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Kaskman, R., Zakharov, S., Shugurov, I., and Ilic, S. (2019, January 27\u201328). Homebreweddb: Rgb-d dataset for 6d pose estimation of 3d objects. Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV) Workshops, Seoul, Korea.","DOI":"10.1109\/ICCVW.2019.00338"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Hodan, T., Haluza, P., Obdr\u017e\u00e1lek, \u0160., Matas, J., Lourakis, M., and Zabulis, X. (2017, January 24\u201331). T-LESS: An RGB-D dataset for 6D pose estimation of texture-less objects. Proceedings of the 2017 IEEE Winter Conference on Applications of Computer Vision (WACV), Santa Rosa, CA, USA.","DOI":"10.1109\/WACV.2017.103"},{"key":"ref_26","unstructured":"Hachenberger, P., and Kettner, L. (2021). 3D Boolean Operations on Nef Polyhedra. CGAL User and Reference Manual, Utrecht University, Faculty of Mathematics and Computer Science Netherlands. [5.1.1 ed.]. CGAL Editorial Board."},{"key":"ref_27","first-page":"586","article-title":"Method for registration of 3-D shapes","volume":"1611","author":"Besl","year":"1992","journal-title":"Sens. Fus. IV Control Paradig. Data Struct."},{"key":"ref_28","unstructured":"Rusinkiewicz, S., and Levoy, M. (June, January 28). Efficient variants of the ICP algorithm. Proceedings of the Third International Conference on 3-D Digital Imaging and Modeling, Quebec City, QC, Canada."},{"key":"ref_29","first-page":"122","article-title":"The OpenCV Library","volume":"120","author":"Bradski","year":"2000","journal-title":"Dr. Dobb\u2019s J. Softw. Tools"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"629","DOI":"10.1364\/JOSAA.4.000629","article-title":"Closed-form solution of absolute orientation using unit quaternions","volume":"4","author":"Horn","year":"1987","journal-title":"J. Opt. Soc. Am. A"},{"key":"ref_31","unstructured":"Matt, J. (2021, July 05). Absolute Orientation\u2014Horn\u2019s Method. Available online: https:\/\/www.mathworks.com\/matlabcentral\/fileexchange\/26186-absolute-orientation-horn-s-method."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1031.e1","DOI":"10.1016\/j.jhsa.2017.07.002","article-title":"Accuracy and early clinical outcome of 3-dimensional planned and guided single-cut osteotomies of malunited forearm bones","volume":"42","author":"Roner","year":"2017","journal-title":"J. Hand Surg."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Guney, F., and Geiger, A. (2015, January 7\u201312). Displets: Resolving stereo ambiguities using object knowledge. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7299044"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Geiger, A., and Wang, C. (2015, January 7\u201310). Joint 3D Object and Layout Inference from a single RGB-D Image. Proceedings of the 37th German Conference on Pattern Recognition (GCPR), Aachen, Germany.","DOI":"10.1007\/978-3-319-24947-6_15"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1016\/j.spinee.2004.10.048","article-title":"Accuracy of single-time, multilevel registration in image-guided spinal surgery","volume":"5","author":"Papadopoulos","year":"2005","journal-title":"Spine J."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"2177","DOI":"10.1109\/TBME.2015.2415731","article-title":"Patient registration using intraoperative stereovision in image-guided open spinal surgery","volume":"62","author":"Ji","year":"2015","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_37","first-page":"268","article-title":"Timing of paired points and surface matching registration in three-dimensional (3D) image-guided spinal surgery","volume":"20","author":"Nottmeier","year":"2007","journal-title":"Clin. Spine Surg."}],"container-title":["Journal of Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2313-433X\/7\/9\/164\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:53:55Z","timestamp":1760165635000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2313-433X\/7\/9\/164"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,8,27]]},"references-count":37,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2021,9]]}},"alternative-id":["jimaging7090164"],"URL":"https:\/\/doi.org\/10.3390\/jimaging7090164","relation":{},"ISSN":["2313-433X"],"issn-type":[{"value":"2313-433X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,8,27]]}}}