Towards markerless computer assisted surgery: Application to total knee arthroplasty.
6-DoF pose estimation
arthroplasty
computer assisted orthopedic surgery
depth sensor
point pair features
total knee arthroplasty
Journal
The international journal of medical robotics + computer assisted surgery : MRCAS
ISSN: 1478-596X
Titre abrégé: Int J Med Robot
Pays: England
ID NLM: 101250764
Informations de publication
Date de publication:
Oct 2021
Oct 2021
Historique:
revised:
25
05
2021
received:
11
09
2020
accepted:
01
06
2021
pubmed:
5
6
2021
medline:
4
9
2021
entrez:
4
6
2021
Statut:
ppublish
Résumé
A new approach is proposed to localise surgical instruments for Computer Assisted Orthopaedic Surgery (CAOS) that aims at overpassing the limitations of conventional CAOS solutions. This approach relies on both a depth sensor and a 6D pose estimation algorithm. The Point-Pair Features (PPF) algorithm was used to estimate the pose of a Patient-Specific Instrument (PSI) for Total Knee Arthroplasty (TKA). Four depth sensors have been compared. Three scores have been computed to assess the performances: The Depth Fitting Error (DFE), the Pose Errors, and the Success Rate. The obtained results demonstrate higher performances for the Microsoft Kinect Azure in terms of DFE. The Occipital Structure core shows better behavior in terms of Pose Errors and Success Rate. This comparative study presents the first depth-sensor based solution allowing the intraoperative markerless localization of surgical instruments in orthopedics.
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
e2296Subventions
Organisme : Agence Nationale de la Recherche
ID : ANR-17-RHUS-0005
Informations de copyright
© 2021 John Wiley & Sons Ltd.
Références
Mason JB, Fehring TK, Estok R, Banel D, Fahrbach K. Meta-analysis of alignment outcomes in computer-assisted total knee arthroplasty surgery. J Arthroplasty. 2007;22(8):1097-1106.
Nguyen D, Ferreira LM, Brownhill JR, et al. Improved accuracy of computer assisted glenoid implantation in total shoulder arthroplasty: an in-vitro randomized controlled trial. J Shoulder Elbow Surg. 2009;18(6):907-914.
Kelley TC, Swank ML. Role of navigation in total hip arthroplasty. J. Bone Jt. Surg. - Ser. 2009; A;91(SUPPL. 1):153-158
Song SJ, Bae DK. Computer-assisted navigation in high tibial osteotomy. CiOS Clin Orthop Surg. 2016;8(4):349-357.
Childers CP, Maggard-Gibbons M. Understanding costs of care in the operating room. JAMA Surg 2018;153(4). Apr.
Macario A. What does one minute of operating room time cost? J Clin Anesth. 2010;22(4):233-236.
Tsukada S, Ogawa H, Nishino M, Kurosaka K, Hirasawa N. Augmented reality-based navigation system applied to tibial bone resection in total knee arthroplasty. J Exp Orthop. 2019;6(1):0-6.
Rodrigues P, Antunes M, Raposo C, Marques P, Fonseca F, Barreto JP. Deep segmentation leverages geometric pose estimation in computer-aided total knee arthroplasty. Healthc Technol Lett. 2019;6(6):226-230.
Qiu B, Liu F, Tang B, et al. Clinical study of 3D imaging and 3D printing technique for patient-specific instrumentation in total knee arthroplasty. J Knee Surg. 2017;30(8):822-828.
Sassoon A, Nam D, Nunley R, Barrack R. Systematic review of patient-specific instrumentation in total knee arthroplasty: new but not improved. Clin Orthop Relat Res. 2015;473:151-158.
Kunz M, Balaketheeswaran S, Ellis RE, Rudan JF. The influence of osteophyte depiction in CT for patient specific guided hip resurfacing procedures. Int J Comput Assist Radiol Surg. 2015;10(6):717-726.
Wong KC. 3D-printed patient-specific applications in orthopedics. Orthop Res Rev. 2016;8(October):57-66.
Arms R. Assistive grasping based on laser-point detection with application to wheelchair-mounted; 2019.
Gomes L, Regina Pereira Bellon O, Silva L. 3D reconstruction methods for digital preservation of cultural heritage: a survey. Pattern Recognit Letture. 2014;50:3-14.
Kadambi A, Bhandari A, Raskar R. 3D Depth Cameras in Vision: Benefits and Limitations of the Hardware. In: Shao L, Han J, Kohli P, Zhang Z, eds. Computer Vision and Machine Learning with RGB-D Sensors (Advances in Computer Vision and Pattern Recognition). Springer; 2014:3-26.
Hodan T, Michel F, Brachmann E, et al. Benchmark for 6D Object pose estimation. In: ECCV. 2018.
Hodan T, Sundermeyer M, Drost B, et al. BOP challenge 2020 on 6D object localization. In: ECCV. 2020.
Drost B, Ulrich M, Navab N, Ilic S. Model globally, match locally: efficient and robust 3D object recognition. In: IEEE Conference on Computer Vision and Pattern Recognition Work. 2010.
Birdal T, Ilic S. Point pair features based object detection and pose estimation revisited. In: Proceedings - 2015 International Conference 3D Vision, 3DV 2015. 2015:527-535.
Hinterstoisser S, Lepetit V, Rajkumar N, Konolige K. Going further with point pair features. In: ECCV. 2017.
Vidal J, Lin C, Martí R. 6D pose estimation using an improved method based on point pair features. In: International Conference on Control. Automation and Robotics. 2018.
V Segal A, Haehnel D, Thrun S. Generalized-ICP (probailistic ICP tutorial). Robot Sci Syst. 2009;2:435.
Xiang Y, Schmidt T, Narayanan V, Fox D. PoseCNN A convolutional neural network for 6D object pose estimation in cluttered scenes, In: RSS. 2018
Hodan T, Matas J, Obdržálek Š. On evaluation of 6D object pose estimation. In: Hua G, Jégou H, eds. Computer Vision - ECCV 2016 Workshops. ECCV 2016. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 9915 LNCS. Springer; 2016:609-619. https://doi.org/10.1007/978-3-319-49409-8_52
Hinterstoisser S, Lepetit V, Ilic S, et al. Model based training, detection and pose estimation of texture-less 3D objects in heavily cluttered scenes. In: Computer Vision - ACCV. Springer; 2012:548-562.
Salas-Moreno RF, Newcombe RA, Strasdat H, Kelly PHJ, Davison AJ, SLAM++: Simultaneous localisation and mapping at the level of objects. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 2013; pp. 1352-1359.