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International Journal of Computer Assisted Radiology and Surgery

Erschienen in:

01.09.2016 | Original Article

Articular surface segmentation using active shape models for intraoperative implant assessment

verfasst von: Joseph Görres, Michael Brehler, Jochen Franke, Sven Y. Vetter, Paul A. Grützner, Hans-Peter Meinzer, Ivo Wolf

Erschienen in: International Journal of Computer Assisted Radiology and Surgery | Ausgabe 9/2016

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Abstract

Purpose

In orthopedic surgeries, it is important to avoid intra-articular implant placements, which increase revision rates and the risk of arthritis. In order to support the intraoperative assessment and correction of surgical implants, we present an automatic detection approach using cone-beam computed tomography (CBCT).

Methods

Multiple active shape models (ASM) with specified articular surface regions are used to isolate the joint spaces. Fast and easy-to-implement methods are integrated in the ASM segmentation to optimize the robustness and accuracy for intraoperative application. A cylinder detection method is applied to determine metal implants. Intersections between articular surfaces and cylinders are detected and used to find intra-articular collisions.

Results

Segmentations of two calcaneal articular surfaces were evaluated on 50 patient images and have shown average surface distance errors of 0.59 and 0.46 mm, respectively. The proposed model-independent segmentation at the specified articular surface regions allowed to significantly decrease the error by 22 and 25 % on average. The method was able to compensate suboptimal initializations for translations of up to 16 mm and rotations of up to 21\(^{\circ }\). In a human cadaver test, articular perforations could be localized with an accuracy of 0.80 mm on average.

Conclusions

A concept for automatic intraoperative detection of intra-articular implants in CBCT images was presented. The results show a reliable segmentation of articular surfaces in retrospective patient data and an accurate localization of misplaced implants in artificially created human cadaver test cases.

Bossa M, Olmos S (2007) Multi-object statistical pose\(+\)shape models. In: International symposium on biomedical imaging: from nano to macro. Institute of Electrical and Electronics Engineers, pp 1204–1207

Brehler M, Görres J, Wolf I, Franke J, von Recum J, Grützner PA, Meinzer HP, Nabers D (2014) Automatic standard plane adjustment on mobile c-arm CT images of the calcaneus using atlas-based feature registration. In: Proceedings of SPIE medical imaging, 90360E. International Society for Optics and Photonics

Cao Q, Thawait G, Gang GJ, Zbijewski W, Reigel T, Brown T, Corner B, Demehri S, Siewerdsen JH (2015) Characterization of 3D joint space morphology using an electrostatic model (with application to osteoarthritis). Phys Med Biol 60(3):947CrossRefPubMedPubMedCentral

Cates J, Fletcher PT, Styner M, Hazlett HC, Whitaker R (2008) Particle-based shape analysis of multi-object complexes. In: Medical image computing and computer assisted intervention—MICCAI 2008. Springer, pp 477–485

Cootes TF, Taylor CJ (1993) Active shape model search using local grey-level models: a quantitative evaluation. In: British machine vision conference, vol 93, pp 639–648. Citeseer

Cootes TF, Taylor CJ (1994) Using grey-level models to improve active shape model search. In: Proceedings of 12th international conference on pattern recognition, vol 1, pp 63–67

Cootes TF, Taylor CJ, Cooper DH, Graham J (1995) Active shape models—their training and application. Comput Vis Image Underst 61(1):38–59CrossRef

De Bruijne M, Lund MT, Tankó LB, Pettersen PC, Nielsen M (2007) Quantitative vertebral morphometry using neighbor-conditional shape models. Med Image Anal 11(5):503–512CrossRefPubMed

Frangi AF, Rueckert D, Schnabel JA, Niessen WJ (2002) Automatic construction of multiple-object three-dimensional statistical shape models: application to cardiac modeling. Trans Med Imag 21(9):1151–1166CrossRef

10.

Franke J, Wendl K, Suda AJ, Giese T, Grützner PA, von Recum J (2014) Intraoperative three-dimensional imaging in the treatment of calcaneal fractures. J Bone Joint Surg 96(9):e72–e77CrossRefPubMed

11.

Geerling J, Kendoff D, Citak M, Zech S, Gardner MJ, Hüfner T, Krettek C, Richter M (2009) Intraoperative 3D imaging in calcaneal fracture care-clinical implications and decision making. J Trauma Acute Care Surg 66(3):768–773CrossRef

12.

Goodall C. (1991) Procrustes methods in the statistical analysis of shape. J R Stat Soc Ser B (Methodol) 53(2):285–339

13.

Görres J, Brehler M, Franke J, Barth K, Vetter SY, Córdova A, Grützner PA, Meinzer HP, Wolf I, Nabers D (2014) Intraoperative detection and localization of cylindrical implants in cone-beam CT image data. Int J Comput Assist Radiol Surg 9(6):1045–1057CrossRefPubMed

14.

Gottschalk S, Lin MC, Manocha D (1996) Obbtree: a hierarchical structure for rapid interference detection. In: Proceedings of the 23rd annual conference on computer graphics and interactive techniques. Association for Computing Machinery, pp 171–180

15.

Heimann T, Münzing S, Meinzer HP, Wolf I (2007) A shape-guided deformable model with evolutionary algorithm initialization for 3D soft tissue segmentation. In: Karssemeijer N, Lelieveldt B (eds) Information processing in medical imaging. Lecture notes in computer science, vol 4584. Springer, Berlin, Heidelberg, pp 1–12

16.

Heimann T, Wolf I, Williams TG, Meinzer HP (2005) 3D active shape models using gradient descent optimization of description length. In: Christensen GE, Sonka M (eds) Information processing in medical imaging. Lecture notes in computer science, vol 3565. Springer, Berlin, Heidelberg, pp 566–577

17.

Janzen D, Connell D, Munk P, Buckley R, Meek R, Schechter M (1992) Intraarticular fractures of the calcaneus: value of CT findings in determining prognosis. AJR Am J Roentgenol 158(6):1271–1274CrossRefPubMed

18.

Kainmüller D (2013) Deformable meshes for accurate automatic segmentation of medical image data. Phd thesis, University of Lübeck

19.

Kainmüller D, Lamecker H, Zachow S, Heller M, Hege H (2008) Multi-object segmentation with coupled deformable models. In: Proceedings of medical image understanding and analysis, pp 34–38

20.

Kendoff D, Citak M, Gardner MJ, Stübig T, Krettek C, Hüfner T (2009) Intraoperative 3D imaging: value and consequences in 248 cases. J Trauma Acute Care Surg 66(1):232–238

21.

Li K, Wu X, Chen DZ, Sonka M (2006) Optimal surface segmentation in volumetric images—a graph-theoretic approach. Trans Pattern Anal Mach Intell 28(1):119–134CrossRef

22.

Nolden M, Zelzer S, Seitel A, Wald D, Müller M, Franz AM, Maleike D, Fangerau M, Baumhauer M, Maier-Hein L, Maier-Hein KH, Meinzer HP, Wolf I (2013) The medical imaging interaction toolkit: challenges and advances. Int J Comput Assist Radiol Surg 8(4):607–620CrossRefPubMed

23.

Qiang M, Chen Y, Zhang K, Li H, Dai H (2014) Measurement of three-dimensional morphological characteristics of the calcaneus using CT image post-processing. J Foot Ankle Res 7(1):19CrossRefPubMedPubMedCentral

24.

Rasoulian A, Rohling R, Abolmaesumi P (2013) Lumbar spine segmentation using a statistical multi-vertebrae anatomical shape and pose model. Trans Med Imag 32(10):1890–1900CrossRef

25.

Richter M, Zech S (2009) Intraoperative 3-dimensional imaging in foot and ankle trauma-experience with a second-generation device (ARCADIS-3D). J Orthop Trauma 23(3):213–220CrossRefPubMed

26.

Rübberdt A, Feil R, Stengel D, Spranger N, Mutze S, Wich M, Ekkernkamp A (2006) The clinical use of the ISO-C (3D) imaging system in calcaneus fracture surgery. Der Unfallchirurg 109(2):112–118CrossRefPubMed

27.

Schwarz T, Heimann T, Lossnitzer D, Mohrhardt C, Steen H, Rietdorf U, Wolf I, Meinzer HP (2010) Multiobject segmentation using coupled shape space models. In: Proceedings of SPIE medical imaging, 76233V. International Society for Optics and Photonics

28.

Wang Q, Chen W, Su Y, Pan J, Zhang Q, Peng A, Wu X, Wang P, Zhang Y (2010) Minimally invasive treatment of calcaneal fracture by percutaneous leverage, anatomical plate, and compression bolts—the clinical evaluation of cohort of 156 patients. J Trauma Acute Care Surg 69(6):1515–1522CrossRef

29.

Yokota F, Okada T, Takao M, Sugano N, Tada Y, Tomiyama N, Sato Y (2013) Automated CT segmentation of diseased hip using hierarchical and conditional statistical shape models. In: Medical image computing and computer assisted intervention—MICCAI 2013. Springer, pp 190–197

Titel: Articular surface segmentation using active shape models for intraoperative implant assessment
verfasst von: Joseph Görres
Michael Brehler
Jochen Franke
Sven Y. Vetter
Paul A. Grützner
Hans-Peter Meinzer
Ivo Wolf
Publikationsdatum: 01.09.2016
Verlag: Springer Berlin Heidelberg
Erschienen in: International Journal of Computer Assisted Radiology and Surgery / Ausgabe 9/2016
Print ISSN: 1861-6410
Elektronische ISSN: 1861-6429
DOI: https://doi.org/10.1007/s11548-015-1316-9

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Articular surface segmentation using active shape models for intraoperative implant assessment