{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T12:46:50Z","timestamp":1775047610648,"version":"3.50.1"},"reference-count":39,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2020,5,8]],"date-time":"2020-05-08T00:00:00Z","timestamp":1588896000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Forschungszentrum Medizintechnik Hamburg","award":["04fmthh16"],"award-info":[{"award-number":["04fmthh16"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Optical tracking systems are widely used, for example, to navigate medical interventions. Typically, they require the presence of known geometrical structures, the placement of artificial markers, or a prominent texture on the target\u2019s surface. In this work, we propose a 6D tracking approach employing volumetric optical coherence tomography (OCT) images. OCT has a micrometer-scale resolution and employs near-infrared light to penetrate few millimeters into, for example, tissue. Thereby, it provides sub-surface information which we use to track arbitrary targets, even with poorly structured surfaces, without requiring markers. Our proposed system can shift the OCT\u2019s field-of-view in space and uses an adaptive correlation filter to estimate the motion at multiple locations on the target. This allows one to estimate the target\u2019s position and orientation. We show that our approach is able to track translational motion with root-mean-squared errors below 0.25 mm and in-plane rotations with errors below 0.3\u00b0. For out-of-plane rotations, our prototypical system can achieve errors around 0.6\u00b0.<\/jats:p>","DOI":"10.3390\/s20092678","type":"journal-article","created":{"date-parts":[[2020,5,8]],"date-time":"2020-05-08T11:26:00Z","timestamp":1588937160000},"page":"2678","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Concept for Markerless 6D Tracking Employing Volumetric Optical Coherence Tomography"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2019-1102","authenticated-orcid":false,"given":"Matthias","family":"Schl\u00fcter","sequence":"first","affiliation":[{"name":"Institute of Medical Technology, Hamburg University of Technology, 21073 Hamburg, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lukas","family":"Glandorf","sequence":"additional","affiliation":[{"name":"Institute of Medical Technology, Hamburg University of Technology, 21073 Hamburg, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Martin","family":"Gromniak","sequence":"additional","affiliation":[{"name":"Institute of Medical Technology, Hamburg University of Technology, 21073 Hamburg, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Thore","family":"Saathoff","sequence":"additional","affiliation":[{"name":"Institute of Medical Technology, Hamburg University of Technology, 21073 Hamburg, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alexander","family":"Schlaefer","sequence":"additional","affiliation":[{"name":"Institute of Medical Technology, Hamburg University of Technology, 21073 Hamburg, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,5,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1002\/rcs.1502","article-title":"Comparison of optical and electromagnetic tracking for navigated lateral skull base surgery","volume":"9","author":"Kral","year":"2013","journal-title":"Int. J. Med. Robot. Comput. Assist. Surg."},{"key":"ref_2","first-page":"810","article-title":"Fiducial Point Placement and the Accuracy of Point-based, Rigid Body Registration","volume":"48","author":"West","year":"2001","journal-title":"Neurosurgery"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"126","DOI":"10.1002\/mrm.27705","article-title":"Markerless high-frequency prospective motion correction for neuroanatomical MRI","volume":"82","author":"Frost","year":"2019","journal-title":"Magn. Reson. Med."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"8753","DOI":"10.1088\/0031-9155\/60\/22\/8753","article-title":"Repurposing the Microsoft Kinect for Windows v2 for external head motion tracking for brain PET","volume":"60","author":"Noonan","year":"2015","journal-title":"Phys. Med. Biol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"810","DOI":"10.1016\/j.ijrobp.2016.01.041","article-title":"Enhanced Optical Head Tracking for Cranial Radiation Therapy: Supporting Surface Registration by Cutaneous Structures","volume":"95","author":"Wissel","year":"2016","journal-title":"Int. J. Radiat. Oncol. Biol. Phys."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"483","DOI":"10.1007\/s11548-011-0653-6","article-title":"Correlation between external and internal respiratory motion: A validation study","volume":"7","author":"Ernst","year":"2012","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"633","DOI":"10.1016\/j.media.2016.09.003","article-title":"Vision-based and marker-less surgical tool detection and tracking: A review of the literature","volume":"35","author":"Bouget","year":"2017","journal-title":"Med. Image Anal."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Verhey, J.T., Haglin, J.M., Verhey, E.M., and Hartigan, D.E. (2020). Virtual, augmented, and mixed reality applications in orthopedic surgery. Int. J. Med. Robot. Comput. Assist. Surg., e2067.","DOI":"10.1002\/rcs.2067"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Chauvet, P., Bourdel, N., Calvet, L., Magnin, B., Teluob, G., Canis, M., and Bartoli, A. (2019). Augmented Reality with Diffusion Tensor Imaging and Tractography during Laparoscopic Myomectomies. J. Minim. Invasive Gynecol.","DOI":"10.1016\/j.jmig.2019.11.007"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Prevost, G.A., Eigl, B., Paolucci, I., Rudolph, T., Peterhans, M., Weber, S., Beldi, G., Candinas, D., and Lachenmayer, A. (2019). Efficiency, Accuracy and Clinical Applicability of a New Image-Guided Surgery System in 3D Laparoscopic Liver Surgery. J. Gastrointest. Surg.","DOI":"10.1007\/s11605-019-04395-7"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1077","DOI":"10.1007\/s00464-019-06855-2","article-title":"Augmented reality in gynecologic laparoscopic surgery: Development, evaluation of accuracy and clinical relevance of a device useful to identify ureters during surgery","volume":"34","author":"Akladios","year":"2019","journal-title":"Surg. Endosc."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"828","DOI":"10.1364\/BOE.8.000828","article-title":"High-speed OCT light sources and systems [Invited]","volume":"8","author":"Klein","year":"2017","journal-title":"Biomed. Opt. Express"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"e0213144","DOI":"10.1371\/journal.pone.0213144","article-title":"Live video rate volumetric OCT imaging of the retina with multi-MHz A-scan rates","volume":"14","author":"Kolb","year":"2019","journal-title":"PLOS ONE"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"622","DOI":"10.1016\/j.jcmg.2015.08.010","article-title":"Heartbeat OCT and Motion-Free 3D In Vivo Coronary Artery Microscopy","volume":"9","author":"Wang","year":"2016","journal-title":"JACC Cardiovasc. Imaging"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2660","DOI":"10.1364\/BOE.8.002660","article-title":"Intravascular optical coherence tomography [Invited]","volume":"8","author":"Bouma","year":"2017","journal-title":"Biomed. Opt. Express"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1117\/1.JBO.23.4.040901","article-title":"Advances in optical coherence tomography in dermatology\u2014A review","volume":"23","author":"Olsen","year":"2018","journal-title":"J. Biomed. Opt."},{"key":"ref_17","first-page":"1","article-title":"Optical Coherence Tomography Guided Laser Cochleostomy: Towards the Accuracy on Tens of Micrometer Scale","volume":"2014","author":"Zhang","year":"2014","journal-title":"BioMed. Res. Int."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Kim, B., and Kim, D.Y. (2016). Enhanced Tissue Ablation Efficiency with a Mid-Infrared Nonlinear Frequency Conversion Laser System and Tissue Interaction Monitoring Using Optical Coherence Tomography. Sensors, 16.","DOI":"10.3390\/s16050598"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Schl\u00fcter, M., Fuh, M.M., Maier, S., Otte, C., Kiani, P., Hansen, N.O., Miller, R.J.D., Schl\u00fcter, H., and Schlaefer, A. (2019, January 23\u201327). Towards OCT-Navigated Tissue Ablation with a Picosecond Infrared Laser (PIRL) and Mass-Spectrometric Analysis. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Berlin, Germany.","DOI":"10.1109\/EMBC.2019.8856808"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Park, B., Lee, S., Bang, H., Kim, B., Park, J., Kim, D., Park, S., and Won, Y. (2017). Image-Guided Laparoscopic Surgical Tool (IGLaST) Based on the Optical Frequency Domain Imaging (OFDI) to Prevent Bleeding. Sensors, 17.","DOI":"10.3390\/s17040919"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"4386","DOI":"10.1364\/OL.43.004386","article-title":"High-speed fiber scanning endoscope for volumetric multi-megahertz optical coherence tomography","volume":"43","author":"Pfeiffer","year":"2018","journal-title":"Opt. Lett."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Zhang, Y., and W\u00f6rn, H. (2014, January 8\u201311). Optical coherence tomography as highly accurate optical tracking system. Proceedings of the IEEE\/ASME International Conference on Advanced Intelligent Mechatronics, Besacon, France.","DOI":"10.1109\/AIM.2014.6878235"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Laves, M.H., Schoob, A., Kahrs, L.A., Pfeiffer, T., Huber, R., and Ortmaier, T. (2017). Feature tracking for automated volume of interest stabilization on 4D-OCT images. SPIE Med. Imaging, 101350W.","DOI":"10.1117\/12.2255090"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"162","DOI":"10.1016\/j.media.2018.03.002","article-title":"A deep learning approach for pose estimation from volumetric OCT data","volume":"46","author":"Gessert","year":"2018","journal-title":"Med. Image Anal."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Laves, M.H., Kahrs, L.A., Ortmaier, T., and Ihler, S. (2019). Deep-learning-based 2.5D flow field estimation for maximum intensity projections of 4D optical coherence tomography. SPIE Med. Imaging, 109510R.","DOI":"10.1117\/12.2512952"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"4734","DOI":"10.1364\/BOE.7.004734","article-title":"Long-range and wide field of view optical coherence tomography for in vivo 3D imaging of large volume object based on akinetic programmable swept source","volume":"7","author":"Song","year":"2016","journal-title":"Biomed. Opt. Express"},{"key":"ref_27","first-page":"100531T","article-title":"Analysis of FDML lasers with meter range coherence","volume":"10053","author":"Pfeiffer","year":"2017","journal-title":"SPIE BiOS"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"3856","DOI":"10.1364\/BOE.8.003856","article-title":"Wide-field high-speed space-division multiplexing optical coherence tomography using an integrated photonic device","volume":"8","author":"Huang","year":"2017","journal-title":"Biomed. Opt. Express"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"327","DOI":"10.1002\/rcs.1425","article-title":"Automatic scanning of large tissue areas in neurosurgery using optical coherence tomography","volume":"8","author":"Finke","year":"2012","journal-title":"Int. J. Med. Robot. Comput. Assist. Surg."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Rajput, O., Antoni, S.T., Otte, C., Saathoff, T., Matth\u00e4us, L., and Schlaefer, A. (2016, January 19\u201321). High accuracy 3D data acquisition using co-registered OCT and kinect. Proceedings of the IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, Baden-Baden, Germany.","DOI":"10.1109\/MFI.2016.7849463"},{"key":"ref_31","unstructured":"Gan, Y., Yao, W., Myers, K.M., and Hendon, C.P. (2014, January 26\u201330). An automated 3D registration method for optical coherence tomography volumes. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Chicago, IL, USA."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Schl\u00fcter, M., Otte, C., Saathoff, T., Gessert, N., and Schlaefer, A. (2019). Feasibility of a markerless tracking system based on optical coherence tomography. SPIE Med. Imaging, 1095107.","DOI":"10.1117\/12.2512178"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Schl\u00fcter, M., Glandorf, L., Sprenger, J., Gromniak, M., Saathoff, T., and Schlaefer, A. (2020, January 3\u20137). High-Speed Markerless Tissue Motion Tracking Using Volumetric Optical Coherence Tomography Images. Proceedings of the IEEE International Symposium on Biomedical Imaging, Iowa City, IA, USA.","DOI":"10.1109\/ISBI45749.2020.9098448"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"583","DOI":"10.1109\/TPAMI.2014.2345390","article-title":"High-speed tracking with kernelized correlation filters","volume":"37","author":"Henriques","year":"2014","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Bolme, D.S., Beveridge, J.R., Draper, B.A., and Lui, Y.M. (2010, January 13\u201318). Visual object tracking using adaptive correlation filters. Proceedings of the Conference on Computer Vision and Pattern Recognition, San Francisco, CA, USA.","DOI":"10.1109\/CVPR.2010.5539960"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"810","DOI":"10.1109\/TPAMI.2004.16","article-title":"The template update problem","volume":"26","author":"Matthews","year":"2004","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"922","DOI":"10.1107\/S0567739476001873","article-title":"A solution for the best rotation to relate two sets of vectors","volume":"32","author":"Kabsch","year":"1976","journal-title":"Acta Crystallogr. Sect. A"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"3944","DOI":"10.1109\/LRA.2018.2858744","article-title":"Towards Robotic Eye Surgery: Marker-Free, Online Hand-Eye Calibration Using Optical Coherence Tomography Images","volume":"3","author":"Zhou","year":"2018","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"614","DOI":"10.1016\/j.procs.2016.06.023","article-title":"Adaptive learning rate for visual tracking using correlation filters","volume":"89","author":"Asha","year":"2016","journal-title":"Procedia Comput. Sci."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/9\/2678\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:26:45Z","timestamp":1760174805000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/9\/2678"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,5,8]]},"references-count":39,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2020,5]]}},"alternative-id":["s20092678"],"URL":"https:\/\/doi.org\/10.3390\/s20092678","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,5,8]]}}}