Abstract
This paper describes the development of a patient-specific spine model through use of active contour segmentation and registration of intraoperative imaging of porcine vertebra augmented with kinematic constraints. The geometric active contours are fully automated and lead to a discrete representation of the image segmentation results. After determining errors within the segmentations, application of reliability theory allows the selection of active contour parameters to obtain best-fit segmentations from a stack of 2D images. The segmented images are then used in conjunction with C-arm fluoroscope images to simulate the result of intraoperative patient-specific model registration including patient and/or structure motion between preoperative and intraoperative scans. The results are validated through comparison of the error within the patient-specific model generated through use of the C-arm images with a model acquired directly from MRI images of the spine after motion. The results are applicable to the development of a wide variety of patient-specific geometric and biomechanical models.
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Kwartowitz DM, Herrell SD, Galloway RL: Toward image-guided robotic surgery: determining intrinsic accuracy of the da Vinci robot. Int J Comput Assist Radiol Surg 1(3):157–165, 2006
Elmore JG, Wells CK, Lee CH, Howard DH, Feinstein AR: Variability in radiologists’ interpretations of mammograms. N Engl J Med 331:1493–1499, 1994
Kundel H: Reader error, object recognition, and visual search. In Proc 2004 SPIE Conference. SPIE, Bellingham, WA, 2004, pp 1–9
Krupinski EA: The future of image perception in radiology: synergy between human and computers. Acad Radiol 10:1–3, 2003
Manning DJ, Gale A, Krupinski EA: Perception research in medical imaging. Br J Radiol 78:683–685, 2005
Abbey CK, Barrett HH: Linear iterative reconstruction algorithms: study of observer performance. In Inf Process Med Imaging. SPIE Bellingham. Klower Academic, WA, Dordrecht, 1995, pp 65–76
Xu C, Prince JL: Snakes, shapes, and gradient vector flow. IEEE Trans Image Process 7(3):359–369, 1998
Kass M, Witkin A, Terzopoulos D: Snakes: active contour models. Int J Comput Vis 1(4):321–331, 1987
Osher S, Paragios N: Geometric level set methods in imaging vision and graphics, 1st edition. Springer, New York, NY, USA, 2003
Malladi R, Sethian JA, Vemuri BC: Shape modeling with front propagation: a level set approach. IEEE Trans Pattern Anal Mach Intell 17(2):158–175, 1995
Tsai A, Yezzi A, Wells W, Tempany C, Tucker D, Fan A, Grimson WE, Willsky A: Shape-based approach to the segmentation of medical imagery using level sets. IEEE Trans Med Imaging 22(2):137–154, 2003
Kirbas C, Quek F: A review of vessel extraction techniques and algorithms. ACM Comput Surv 36(2):81–121, 2004
Suri JS, Setarehdan SK, Singh S Eds: Advanced algorithmic approaches to medical image segmentation: state-of-the-art applications in cardiology, neurology, mammography, and pathology, ch 3. Springer-Verlag London, London, England, UK, 2002, pp 148–161
Suri JS: Advanced algorithmic approaches to medical image segmentation: state-of-the-art applications in cardiology, neurology, mammography, and pathology, ch 8, pp 416–421. Springer, London, UK, 2002
Pham DL, Xu C, Prince JL: Current methods in medical image segmentation. Annu Rev Biomed Eng 2(1):315–337, 2000
Hill DLG, Batchelor PG, Holden M, Hawkes DJ: Medical image registration. Phys Med Biol 46:R1–R45, 2001
Hemler PF, Napel S, Sumanaweera TS, Pichumani R, VanDenElsen PA, Martin D, Drace J, Adler JR: Registration error quantification of a surface-based multimodality image fusion system. Med Phys 22(7):1049–1056, 1995
Maintz JBA, Viergever MA: A survey of medical image registration. Med Image Anal 2(1):1–36, 1998
Banik S, Rangayyan RM, Boag GS: Automatic segmentation of the ribs, the vertebral column, and the spinal canal in pediatric computed tomographic images. J Dig Img 23(3):301–322, 2010
Siewerdsen JH, Moseley DJ, Burch S, Bisland SK, Bogaards A, Wilson BC, Jaffray DA: Volume ct with a flat-panel detector on a mobile, isocentric C-arm: pre-clinical investigation in guidance of minimally invasive surgery. Med Phys 32(1):241–254, 2005
Fahrig R, Butts K, Wen Z, Saunders R, Kee ST, Sze DY, Daniel BL, Laerum F, Pelc NJ: Truly hybrid interventional MR/X-Ray system: investigation of in vivo applications. Acad Radiol 8:1200–1207, 2001
Froelich JJ, El-Sheik M, Wagner HJ, Schenbach S, Scherf C, Klose KJ: Feasibility of C-arm-supported ct fluoroscopy in percutaneous abscess drainage procedures. Cardiovasc Intervent Radiol 23:423–430, 2000
Mitton D, Zhao K, Bertrand S, Zhao C, Laporte S, Yang C, An KN, Skalli W: 3D reconstruction of the ribs from lateral and frontal X-ray s in comparison to 3D CT-scan reconstruction. J Biomech 41:706–710, 2008
Templeton A, Cody D, Liebschner M: Updating a 3-D vertebral body finite element model using 2-D images. Med Eng Phys 26(4):329–333, 2004
Geman S, McClure DE: Bayesian image analysis: an application to single photon emission tomography. In Proc Amer Stat Assoc Statistical Computing Section. 1985, pp 12–18
Caselles V, Kimmel R, Sapiro G: Geodesic active contours. Int J Comput Vis 22(1):61–79, 1997
Kichenassamy S, Kumar A, Olver P, Tannenbaum A, Yezzi A: Gradient flows and geometric active contour models. In Proceedings of the Fifth International Conference on Computer Vision. IEEE Computer Society, Washington, DC, USA, 1995, pp 810–815
Hamming RW: Error detecting and error correcting codes. Bell Syst Tech J 26(2):147–160, 1950
Cotter SA: A screening design for factorial experiments with interactions. Biometrika 66(2):317–320, 1979
McKay MD, Beckman RJ, Conover WJ: A comparison of three methods for selecting values of input variables in the analysis of output from a computer code. Technometrics 21(2):239–245, 1979
Nocedal J, Wright SJ: Numerical optimization. Springer series in operations research. Springer, New York, NY, USA, 1999, pp 53–55
Yezzi A, Kichenassamy S, Kumar A, Olver P, Tannenbaum A: A geometric snake model for segmentation of medical imagery. IEEE Trans Med Imaging 16(2):199–209, 1997
Udupa JK, Herman GT Eds: 3D imaging in medicine, ch 1, 2nd edition. CRC Press LLC, Boca Raton, FL, USA, 2002, pp 1–67
Sapiro G: Geometric partial differential equations and image analysis, ch 1. Cambridge University Press, New York, NY, USA, 2001, pp 44–63
Epstein CL, Gage M: Wave motion: theory, modeling, and computation, v 7 of Math Sci Res Inst Publ, ch 2. Springer, New York, NY, USA, 1987, pp 15–59
Osher SJ, Sethian JA: Fronts propagating with curvature-dependent speed: algorithms based on Hamilton–Jacobi formulations. J Comput Phys 79(1):12–49, 1988
Boyer CB: A history of mathematics, 2nd edition. New York, NY, USA: Wiley, 1991, Revised by Uta C. Merzbach
Li C, Xu C, Gui C, Fox MD: Level set evolution without re-initialization: a new variational approach. In Proceedings of the 2005 IEEE Computer Society International Conference on Computer Vision and Pattern Recognition. IEEE Computer Society, Washington, DC, USA, 2005, pp 430–436
Wade JA: An investigation of ovine lumbar kinematics using the Purdue spine simulator. Master’s thesis. School of Mechanical Engineering, Purdue University, West Lafayette, 2005
Wilke HJ, Kettler A, Claes LE: Are sheep spines a valid biomechanical model for human spines? Spine 22(20):2365–2374, 1997
Ryan TP Ed.: Modern experimental design. Wiley series in probability and statistics. Wiley-Interscience, Hoboken, NJ, USA, 2007
Owen AB: A central limit theorem for latin hypercube sampling. J R Stat Soc 54:541–551, 1992
Loh WL: On latin hypercube sampling. The Annals of Statistics 24:2058–2080, 1996
Iman RL, Helton JC: The repeatability of uncertainty and sensitivity analyses for complex probabilistic risk assessments. Risk Anal 11:591–606, 1991
Zurada JM, Malinowski A, Usui S: Perturbation method for deleting redundant inputs of perceptron networks, 1997
Pannell DJ: Sensitivity analysis of normative economic models: theoretical framework and practical strategies. Agric Econ 16(2):139–152, 1997
Galle B: Development of a mechanical spine simulator and determination of lumbar kinematics. Master’s thesis. School of Mechanical Engineering, Purdue University, West Lafayette, 2005
Acknowledgment
The authors would like to acknowledge the National Science Foundation (NSF) for providing funding support to the team members via the Graduate Research Fellowship (GRF) Program.
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Strickland, C.G., Aguiar, D.E., Nauman, E.A. et al. Development of Subject-Specific Geometric Spine Model through Use of Automated Active Contour Segmentation and Kinematic Constraint-Limited Registration. J Digit Imaging 24, 926–942 (2011). https://doi.org/10.1007/s10278-010-9336-z
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DOI: https://doi.org/10.1007/s10278-010-9336-z