{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,8]],"date-time":"2026-01-08T21:41:34Z","timestamp":1767908494856,"version":"3.49.0"},"reference-count":47,"publisher":"Springer Science and Business Media LLC","issue":"8","license":[{"start":{"date-parts":[[2025,5,23]],"date-time":"2025-05-23T00:00:00Z","timestamp":1747958400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,5,23]],"date-time":"2025-05-23T00:00:00Z","timestamp":1747958400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100000780","name":"European Commission","doi-asserted-by":"publisher","award":["101137416"],"award-info":[{"award-number":["101137416"]}],"id":[{"id":"10.13039\/501100000780","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003759","name":"Universidad Polit\u00e9cnica de Madrid","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100003759","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J CARS"],"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:sec>\n            <jats:title>Purpose<\/jats:title>\n            <jats:p>This study proposes a generalization of markerless patient registration in image-guided neurosurgery based on depth information. The work builds on previous research to evaluate the performance of a range of commercial depth cameras and two different registration algorithms in this context.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Methods<\/jats:title>\n            <jats:p>A multimodal experimental setup was used, testing five depth cameras in seven configurations. Fiducial registration error (FRE) and target registration error (TRE) metrics were calculated using iterative closest point (ICP) and deep global registration (DGR) algorithms. A phantom head model was used to simulate clinical conditions, with cameras positioned to capture the face and craniotomy regions.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Results<\/jats:title>\n            <jats:p>The best-performing cameras, such as the D405 and Zed-M+, achieved TRE values as low as 2.36 \u00b1 0.46 mm and 2.49 \u00b1 0.35 mm, respectively, compared to manual registration that obtains a 1.37 mm error. Cameras equipped with texture projectors or enhanced depth refinement demonstrated improved performance. The proposed methodology effectively characterized the suitability of the camera for the registration tasks.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Conclusion<\/jats:title>\n            <jats:p>This study validates an adaptable and reproducible framework to evaluate depth cameras in neurosurgical scenarios, highlighting D405 and Zed-M + as reliable options. Future work will focus on improving depth quality through hardware and algorithmic improvements. The experimental data and the accompanying code were made publicly available to ensure reproducibility.<\/jats:p>\n          <\/jats:sec>","DOI":"10.1007\/s11548-025-03416-y","type":"journal-article","created":{"date-parts":[[2025,5,22]],"date-time":"2025-05-22T23:21:39Z","timestamp":1747956099000},"page":"1759-1769","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Benchmarking commercial depth sensors for intraoperative markerless registration in neurosurgery applications"],"prefix":"10.1007","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7000-6289","authenticated-orcid":false,"given":"Manuel","family":"Villa","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8767-6596","authenticated-orcid":false,"given":"Jaime","family":"Sancho","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3236-1236","authenticated-orcid":false,"given":"Gonzalo","family":"Rosa-Olmeda","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0280-3440","authenticated-orcid":false,"given":"Miguel","family":"Chavarrias","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6096-1511","authenticated-orcid":false,"given":"Eduardo","family":"Juarez","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2411-9132","authenticated-orcid":false,"given":"Cesar","family":"Sanz","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,5,23]]},"reference":[{"key":"3416_CR1","unstructured":"3D Slicer Community (2023a) 3D Slicer. https:\/\/www.slicer.org\/, accessed: 2025-01-09"},{"issue":"6","key":"3416_CR2","doi-asserted-by":"publisher","first-page":"543","DOI":"10.1007\/s00701-004-0229-0","volume":"146","author":"B Albayrak","year":"2004","unstructured":"Albayrak B, Samdani AF, Black PM (2004) Intra-operative magnetic resonance imaging in neurosurgery. Acta Neurochirurgica 146(6):543\u2013557. https:\/\/doi.org\/10.1007\/s00701-004-0229-0","journal-title":"Acta Neurochirurgica"},{"key":"3416_CR3","unstructured":"Baheti B, Chakrabarty S, et\u00a0al (2024a) The brain tumor sequence registration (brats-reg) challenge: Establishing correspondence between pre-operative and follow-up mri scans of diffuse glioma patients. arXiv: https:\/\/arxiv.org\/abs\/2112.06979"},{"key":"3416_CR4","doi-asserted-by":"publisher","DOI":"10.1177\/20552076221074122","author":"JM Bhalodiya","year":"2022","unstructured":"Bhalodiya JM, Lim Choi Keung SN, Arvanitis TN (2022) Magnetic resonance image-based brain tumour segmentation methods: A systematic review. Digital Health. https:\/\/doi.org\/10.1177\/20552076221074122","journal-title":"Digital Health"},{"key":"3416_CR5","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2022.102531","volume":"81","author":"J Bierbrier","year":"2022","unstructured":"Bierbrier J, Gueziri HE, Collins DL (2022) Estimating medical image registration error and confidence: A taxonomy and scoping review. Medical Image Analysis 81:102531. https:\/\/doi.org\/10.1016\/j.media.2022.102531","journal-title":"Medical Image Analysis"},{"issue":"6","key":"3416_CR6","doi-asserted-by":"publisher","first-page":"1109","DOI":"10.1007\/s11548-023-02887-1","volume":"18","author":"L Burger","year":"2023","unstructured":"Burger L, Sharan L, Karl R, Wang C, Karck M, De Simone R, Wolf I, Romano G, Engelhardt S (2023) Comparative evaluation of three commercially available markerless depth sensors for close-range use in surgical simulation. International Journal of Computer Assisted Radiology and Surgery 18(6):1109\u20131118. https:\/\/doi.org\/10.1007\/s11548-023-02887-1","journal-title":"International Journal of Computer Assisted Radiology and Surgery"},{"key":"3416_CR7","doi-asserted-by":"publisher","unstructured":"Burger W (2016) Zhang\u2019s camera calibration algorithm: In-depth tutorial and implementation. Tech. Rep. HGB16-05, University of Applied Sciences Upper Austria, School of Informatics, Communications and Media, Dept. of Digital Media, Hagenberg, Austria, https:\/\/doi.org\/10.13140\/RG.2.1.1166.1688\/1","DOI":"10.13140\/RG.2.1.1166.1688\/1"},{"issue":"3","key":"3416_CR8","doi-asserted-by":"publisher","first-page":"196","DOI":"10.1159\/000511114","volume":"99","author":"JF Burke","year":"2021","unstructured":"Burke JF, Tanzillo D, Starr PA, Lim DA, Larson PS (2021) Ct and mri image fusion error: An analysis of co-registration error using commercially available deep brain stimulation surgical planning software. Stereotactic and Functional Neurosurgery 99(3):196\u2013202. https:\/\/doi.org\/10.1159\/000511114","journal-title":"Stereotactic and Functional Neurosurgery"},{"key":"3416_CR9","doi-asserted-by":"publisher","unstructured":"Cao J, Chen B, Liu K (2023c) Comparative study of feature-based surface matching automatic coarse registration algorithms for neuronavigation. In: Intelligent Robotics and Applications. Springer Nature Singapore, Singapore, pp 506\u2013517, https:\/\/doi.org\/10.1007\/978-981-99-6480-2_42","DOI":"10.1007\/978-981-99-6480-2_42"},{"key":"3416_CR10","doi-asserted-by":"publisher","first-page":"e196","DOI":"10.1016\/j.wneu.2023.11.073","volume":"182","author":"E Cekic","year":"2024","unstructured":"Cekic E, Pinar E, Pinar M, Dagcinar A (2024) Deep learning-assisted segmentation and classification of brain tumor types on magnetic resonance and surgical microscope images. World Neurosurgery 182:e196\u2013e204. https:\/\/doi.org\/10.1016\/j.wneu.2023.11.073","journal-title":"World Neurosurgery"},{"key":"3416_CR11","unstructured":"Choy CB, Dong W, Koltun V (2020a) Deep global registration. CoRR abs\/2004.11540. https:\/\/arxiv.org\/abs\/2004.11540"},{"key":"3416_CR12","doi-asserted-by":"publisher","unstructured":"Connelly JM, Prah MA, Santos-Pinheiro F, Mueller W, Cochran E, Schmainda KM (2021b) Magnetic resonance imaging mapping of brain tumor burden: Clinical implications for neurosurgical management: Case report. Neurosurgery Open 2(4):okab029. https:\/\/doi.org\/10.1093\/neuopn\/okab029","DOI":"10.1093\/neuopn\/okab029"},{"issue":"2","key":"3416_CR13","doi-asserted-by":"publisher","first-page":"E21","DOI":"10.3171\/2021.5.FOCUS21184","volume":"51","author":"YS Dho","year":"2021","unstructured":"Dho YS, Park SJ, Choi H, Kim Y, Moon HC, Kim KM, Kang H, Lee EJ, Kim MS, Kim JW, Kim YH, Kim YG, Park CK (2021) Development of an inside-out augmented reality technique for neurosurgical navigation. Neurosurgical Focus 51(2):E21. https:\/\/doi.org\/10.3171\/2021.5.FOCUS21184","journal-title":"Neurosurgical Focus"},{"issue":"3","key":"3416_CR14","doi-asserted-by":"publisher","first-page":"363","DOI":"10.1007\/s11548-016-1478-0","volume":"12","author":"S Drouin","year":"2017","unstructured":"Drouin S, Kochanowska A, Kersten-Oertel M, Gerard IJ, Zelmann R, De Nigris D, B\u00e9riault S, Arbel T, Sirhan D, Sadikot AF, Hall JA, Sinclair DS, Petrecca K, DelMaestro RF, Collins DL (2017) Ibis: an or ready open-source platform for image-guided neurosurgery. International Journal of Computer Assisted Radiology and Surgery 12(3):363\u2013378. https:\/\/doi.org\/10.1007\/s11548-016-1478-0. (epub 2016 Aug 31)","journal-title":"International Journal of Computer Assisted Radiology and Surgery"},{"issue":"1","key":"3416_CR15","doi-asserted-by":"publisher","first-page":"28683","DOI":"10.1038\/s41598-024-80001-4","volume":"14","author":"MK Gakhal","year":"2024","unstructured":"Gakhal MK, Bakshi A, Gu M, Khambay BS (2024) A study to determine the three-dimensional (3d) facial shape characteristics for a successful ffp3 mask fit. Scientific Reports 14(1):28683. https:\/\/doi.org\/10.1038\/s41598-024-80001-4","journal-title":"Scientific Reports"},{"key":"3416_CR16","doi-asserted-by":"publisher","unstructured":"Gsaxner C, Li J, Pepe A, Schmalstieg D, Egger J (2021d) Inside-out instrument tracking for surgical navigation in augmented reality. In: Proceedings of the 27th ACM Symposium on Virtual Reality Software and Technology. Association for Computing Machinery, New York, NY, USA, VRST \u201921, https:\/\/doi.org\/10.1145\/3489849.3489863","DOI":"10.1145\/3489849.3489863"},{"key":"3416_CR17","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2021.102292","volume":"75","author":"R Han","year":"2022","unstructured":"Han R, Jones C, Lee J, Wu P, Vagdargi P, Uneri A, Helm P, Luciano M, Anderson W, Siewerdsen J (2022) Deformable mr-ct image registration using an unsupervised, dual-channel network for neurosurgical guidance. Medical Image Analysis 75:102292. https:\/\/doi.org\/10.1016\/j.media.2021.102292","journal-title":"Medical Image Analysis"},{"key":"3416_CR18","doi-asserted-by":"publisher","first-page":"64708","DOI":"10.1109\/ACCESS.2021.3075628","volume":"9","author":"X Hu","year":"2021","unstructured":"Hu X, Liu H, Baena FRY (2021) Markerless navigation system for orthopaedic knee surgery: A proof of concept study. IEEE Access 9:64708\u201364718. https:\/\/doi.org\/10.1109\/ACCESS.2021.3075628","journal-title":"IEEE Access"},{"key":"3416_CR19","unstructured":"Intel RealSense (2018a) Depth Camera D415. https:\/\/www.intelrealsense.com\/depth-camera-d415\/, Accessed: 2025-01-07"},{"key":"3416_CR20","unstructured":"Intel RealSense (2018b) Depth Camera D435f. https:\/\/www.intelrealsense.com\/depth-camera-d435f\/, accessed: 2025-01-07"},{"key":"3416_CR21","unstructured":"Intel RealSense (2022d) Depth Camera D405. https:\/\/www.intelrealsense.com\/depth-camera-d405\/, Accessed: 2025-01-07"},{"issue":"6","key":"3416_CR22","doi-asserted-by":"publisher","first-page":"1179","DOI":"10.4103\/neurol-india.Neurol-India-D-23-00012","volume":"72","author":"P Kalgudi","year":"2024","unstructured":"Kalgudi P, Bharadwaj S, Chakrabarti D, Bhadrinarayan V, Uppar AM, Prasad C (2024) Correlation of preoperative hippocampal volume measured with magnetic resonance imaging and emergence from general anaesthesia in elective neurosurgical patients: An observational study. Neurology India 72(6):1179\u20131185. https:\/\/doi.org\/10.4103\/neurol-india.Neurol-India-D-23-00012","journal-title":"Neurology India"},{"issue":"3","key":"3416_CR23","doi-asserted-by":"publisher","first-page":"871","DOI":"10.1002\/jmri.28332","volume":"57","author":"H Kang","year":"2023","unstructured":"Kang H, Witanto JN, Pratama K, Lee D, Choi KS, Choi SH, Kim KM, Kim MS, Kim JW, Kim YH, Park SJ, Park CK (2023) Fully automated mri segmentation and volumetric measurement of intracranial meningioma using deep learning. Journal of Magnetic Resonance Imaging 57(3):871\u2013881. https:\/\/doi.org\/10.1002\/jmri.28332","journal-title":"Journal of Magnetic Resonance Imaging"},{"key":"3416_CR24","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TIM.2023.3241059","volume":"72","author":"D Li","year":"2023","unstructured":"Li D, Zhu M, Wang S, Hu Y, Yuan F, Yu J (2023) A vision-based navigation system with markerless image registration and position-sensing localization for oral and maxillofacial surgery. IEEE Transactions on Instrumentation and Measurement 72:1\u201311. https:\/\/doi.org\/10.1109\/TIM.2023.3241059","journal-title":"IEEE Transactions on Instrumentation and Measurement"},{"key":"3416_CR25","doi-asserted-by":"publisher","unstructured":"Li H, Yan W, Liu D, Qian L, Yang Y, Liu Y, Zhao Z, Ding H, Wang G (2024e) Evd surgical guidance with retro-reflective tool tracking and spatial reconstruction using head-mounted augmented reality device. IEEE Transactions on Visualization and Computer Graphics pp 1\u201315. https:\/\/doi.org\/10.1109\/TVCG.2024.3518258","DOI":"10.1109\/TVCG.2024.3518258"},{"key":"3416_CR26","doi-asserted-by":"publisher","first-page":"68030","DOI":"10.1109\/ACCESS.2020.2986470","volume":"8","author":"P Li","year":"2020","unstructured":"Li P, Wang R, Wang Y, Tao W (2020) Evaluation of the icp algorithm in 3d point cloud registration. IEEE Access 8:68030\u201368048. https:\/\/doi.org\/10.1109\/ACCESS.2020.2986470","journal-title":"IEEE Access"},{"key":"3416_CR27","doi-asserted-by":"publisher","unstructured":"Liang C, Li M, Gong J, Zhang B, Lin C, He H, Zhang K, Guo Y (2019) A new application of ultrasound-magnetic resonance multimodal fusion virtual navigation in glioma surgery. Annals of Translational Medicine 7(23). https:\/\/doi.org\/10.21037\/atm.2019.11.113","DOI":"10.21037\/atm.2019.11.113"},{"key":"3416_CR28","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2023.103027","volume":"91","author":"F Liebmann","year":"2024","unstructured":"Liebmann F, von Atzigen M, St\u00fctz D, Wolf J, Zingg L, Suter D, Cavalcanti NA, Leoty L, Esfandiari H, Snedeker JG, Oswald MR, Pollefeys M, Farshad M, F\u00fcrnstahl P (2024) Automatic registration with continuous pose updates for marker-less surgical navigation in spine surgery. Medical Image Analysis 91:103027. https:\/\/doi.org\/10.1016\/j.media.2023.103027","journal-title":"Medical Image Analysis"},{"issue":"6","key":"3416_CR29","doi-asserted-by":"publisher","DOI":"10.1002\/rcs.70030","volume":"20","author":"Z Liu","year":"2024","unstructured":"Liu Z, Yang Z, Jiang S, Zhou Z (2024) A spatial registration method based on point cloud and deep learning for augmented reality neurosurgical navigation. The International Journal of Medical Robotics and Computer Assisted Surgery 20(6):e70030. https:\/\/doi.org\/10.1002\/rcs.70030","journal-title":"The International Journal of Medical Robotics and Computer Assisted Surgery"},{"key":"3416_CR30","unstructured":"Luxonis (2022e) Oak-D SR. https:\/\/shop.luxonis.com\/products\/oak-d-sr, accessed: 2025-01-07"},{"key":"3416_CR31","doi-asserted-by":"publisher","unstructured":"MacCormac O, Noonan P, Janatka M, Horgan CC, Bahl A, Qiu J, Elliot M, Trotouin T, Jacobs J, Patel S, Bergholt MS, Ashkan K, Ourselin S, Ebner M, Vercauteren T, Shapey J (2023) Lightfield hyperspectral imaging in neuro-oncology surgery: an ideal 0 and 1 study. Frontiers in Neuroscience 17. https:\/\/doi.org\/10.3389\/fnins.2023.1239764","DOI":"10.3389\/fnins.2023.1239764"},{"issue":"7","key":"3416_CR32","doi-asserted-by":"publisher","first-page":"3578","DOI":"10.1109\/TVCG.2023.3238309","volume":"30","author":"A Martin-Gomez","year":"2024","unstructured":"Martin-Gomez A, Li H, Song T, Yang S, Wang G, Ding H, Navab N, Zhao Z, Armand M (2024) Sttar: Surgical tool tracking using off-the-shelf augmented reality head-mounted displays. IEEE Transactions on Visualization and Computer Graphics 30(7):3578\u20133593. https:\/\/doi.org\/10.1109\/TVCG.2023.3238309","journal-title":"IEEE Transactions on Visualization and Computer Graphics"},{"key":"3416_CR33","unstructured":"Microsoft (2020c) Azure Kinect DK. https:\/\/azure.microsoft.com\/es-es\/products\/kinect-dk\/, accessed: 2025-01-09"},{"key":"3416_CR34","unstructured":"OpenCV (2022f) Camera calibration with opencv. https:\/\/docs.opencv.org\/4.x\/da\/d0d\/tutorial_camera_calibration_pattern.html, accessed: 2025-01-07"},{"key":"3416_CR35","unstructured":"OptiTrack (2018c) Flex 3. https:\/\/optitrack.com\/cameras\/flex-3\/, accessed: 2025-01-07"},{"key":"3416_CR36","unstructured":"Photoneo (2022g) MotionCam 3D S. https:\/\/www.photoneo.com\/es\/products\/motioncam-3d-s\/, accessed: 2025-01-07"},{"key":"3416_CR37","doi-asserted-by":"publisher","first-page":"479","DOI":"10.1007\/s11060-020-03667-6","volume":"151","author":"CM Rogers","year":"2021","unstructured":"Rogers CM, Jones PS, Weinberg JS (2021) Intraoperative mri for brain tumors. Journal of neuro-oncology 151:479\u2013490. https:\/\/doi.org\/10.1007\/s11060-020-03667-6","journal-title":"Journal of neuro-oncology"},{"key":"3416_CR38","doi-asserted-by":"publisher","unstructured":"Sancho J, Villa M, Chavarr\u00edas M, Juarez E, Lagares A, Sanz C (2023) Slimbrain: Augmented reality real-time acquisition and processing system for hyperspectral classification mapping with depth information for in-vivo surgical procedures. Journal of Systems Architecture 140. https:\/\/doi.org\/10.1016\/j.sysarc.2023.102893","DOI":"10.1016\/j.sysarc.2023.102893"},{"key":"3416_CR39","unstructured":"SciPy Community (2013) SciPy: Scientific Computing Tools for Python. https:\/\/scipy.org\/, accessed: 2025-01-09"},{"key":"3416_CR40","unstructured":"Shining 3D (2018d) EinScan Pro 2X v2. https:\/\/www.einscan.com\/escaneres-3d-portatiles\/einscan-pro-2x-v2-es\/, accessed: 2025-01-07"},{"key":"3416_CR41","doi-asserted-by":"publisher","unstructured":"Song J, Zheng J, Li P, Lu X, Zhu G, Shen P (2021) An effective multimodal image fusion method using mri and pet for alzheimer\u2019s disease diagnosis. Frontiers in Digital Health 3. https:\/\/doi.org\/10.3389\/fdgth.2021.637386","DOI":"10.3389\/fdgth.2021.637386"},{"key":"3416_CR42","unstructured":"Stereolabs (2017b) ZED Mini. https:\/\/www.stereolabs.com\/en-es\/store\/products\/zed-mini, Accessed: 2025-01-07"},{"key":"3416_CR43","doi-asserted-by":"publisher","unstructured":"Strobl KH, Hirzinger G (2011) More accurate pinhole camera calibration with imperfect planar target. In: 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops), pp 1068\u20131075, https:\/\/doi.org\/10.1109\/ICCVW.2011.6130369","DOI":"10.1109\/ICCVW.2011.6130369"},{"key":"3416_CR44","unstructured":"Ultralytics Team (2024i) Ultralytics GitHub Repository. https:\/\/github.com\/ultralytics\/ultralytics, accessed: 2025-01-09"},{"issue":"7","key":"3416_CR45","doi-asserted-by":"publisher","first-page":"1367","DOI":"10.1007\/s11548-024-03102-5","volume":"19","author":"M Villa","year":"2024","unstructured":"Villa M, Sancho J, Rosa G, Chavarrias M, Juarez E, Sanz C (2024) Hypermri: hyperspectral and magnetic resonance fusion methodology for neurosurgery applications. International Journal of Computer Assisted Radiology and Surgery 19(7):1367\u20131374. https:\/\/doi.org\/10.1007\/s11548-024-03102-5","journal-title":"International Journal of Computer Assisted Radiology and Surgery"},{"key":"3416_CR46","unstructured":"VTK Community (2024k) The Visualization Toolkit (VTK). https:\/\/vtk.org\/, accessed: 2025-01-09"},{"issue":"1","key":"3416_CR47","doi-asserted-by":"publisher","DOI":"10.1002\/rcs.2318","volume":"18","author":"P Winnand","year":"2022","unstructured":"Winnand P, Ayoub N, Redick T, Gesenhues J, Heitzer M, Peters F, Raith S, Abel D, H\u00f6lzle F, Modabber A (2022) Navigation of iliac crest graft harvest using markerless augmented reality and cutting guide technology: A pilot study. The International Journal of Medical Robotics and Computer Assisted Surgery 18(1):e2318. https:\/\/doi.org\/10.1002\/rcs.2318","journal-title":"The International Journal of Medical Robotics and Computer Assisted Surgery"}],"container-title":["International Journal of Computer Assisted Radiology and Surgery"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11548-025-03416-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11548-025-03416-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11548-025-03416-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,13]],"date-time":"2025-08-13T05:34:05Z","timestamp":1755063245000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11548-025-03416-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5,23]]},"references-count":47,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2025,8]]}},"alternative-id":["3416"],"URL":"https:\/\/doi.org\/10.1007\/s11548-025-03416-y","relation":{},"ISSN":["1861-6429"],"issn-type":[{"value":"1861-6429","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,5,23]]},"assertion":[{"value":"10 January 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 April 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 May 2025","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.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}