Abstract
Medical imaging technologies have allowed for in vivo exploration and evaluation of the human musculoskeletal system. Three-dimensional bone models generated using image-segmentation techniques provide a means to optimize individualized orthopedic surgical procedures using engineering analyses. However, many of the current segmentation techniques are not clinically practical due to the required time and human intervention. As a proof of concept, we demonstrate the use of an expectation maximization (EM) algorithm to segment the hand phalanx bones, and hypothesize that this semi-automated technique will improve the efficiency while providing similar definitions as compared to a manual rater. Our results show a relative overlap of the proximal, middle, and distal phalanx bones of 0.83, 0.79, and 0.72 for the EM technique when compared to validated manual segmentations. The EM segmentations were also compared to 3D surface scans of the cadaveric specimens, which resulted in distance maps showing an average distance for the proximal, middle, and distal phalanx bones of 0.45, 0.46, and 0.51 mm, respectively. The EM segmentation improved on the segmentation speed of the manual techniques by a factor of eight. Overall, the manual segmentations had greater relative overlap metric values, which suggests that the manual segmentations are a better fit to the actual surface of the bone. As shown by the comparison to the bone surface scans, the EM technique provides a similar representation of the anatomic structure and offers an increase in efficiency that could help to reduce the time needed for defining anatomical structures from CT scans.
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Sebastian TB, Tek H, Crisco JJ, Kimia BB: Segmentation of carpal bones from CT images using skeletally coupled deformable models. Med Image Anal 7:21–45, 2003
Zoroofi RA, Sato Y, Sasama T, et al: Automated segmentation of acetabulum and femoral head from 3-D CT images. IEEE Trans Inf Technol Biomed 7:329–343, 2003
Gelaude F, Vander Sloten J, Lauwers B: Semi-automated segmentation and visualisation of outer bone cortex from medical images. Comput Methods Biomech Biomed Engin 9:65–77, 2006
Ehrhardt J, Handels H, Malina T, Strathmann B, Plotz W, Poppl SJ: Atlas-based segmentation of bone structures to support the virtual planning of hip operations. Int J Med Inform 64:439–447, 2001
Mastmeyer A, Engelke K, Fuchs C, Kalender WA: A hierarchical 3D segmentation method and the definition of vertebral body coordinate systems for QCT of the lumbar spine. Med Image Anal 10:560–577, 2006
Staal J, van Ginneken B, Viergever MA: Automatic rib segmentation and labeling in computed tomography scans using a general framework for detection, recognition and segmentation of objects in volumetric data. Med Image Anal 11:35–46, 2007
Dufresne T: Segmentation techniques for analysis of bone by three-dimensional computed tomographic imaging. Technol Health Care 6:351–359, 1998
Burnett SS, Starkschalla G, Stevens CW, Liao Z: A deformable-model approach to semi-automatic segmentation of CT images demonstrated by application to the spinal canal. Med Phys 31:251–263, 2004
Li Y, Hong B, Gao S, Liu K: Bone segmentation in human CT images. Sheng Wu Yi Xue Gong Cheng Xue Za Zhi 21:169–173, 2004
Rueda S, Gil JA, Pichery R, Alcaniz M: Automatic segmentation of jaw tissues in CT using active appearance models and semi-automatic landmarking. Med Image Comput Comput Assist Interv Int Conf Med Image Comput Comput Assist Interv 9:167–174, 2006
Saparin P, Thomsen JS, Kurths J, Beller G, Gowin W: Segmentation of bone CT images and assessment of bone structure using measures of complexity. Med Phys 33:3857–3873, 2006
Wang LI, Greenspan M, Ellis R: Validation of bone segmentation and improved 3-D registration using contour coherency in CT data. IEEE Trans Med Imaging 25:324–334, 2006
Gassman EE, Powell SM, Kallemeyn NA, et al: Automated bony region identification using artificial neural networks: reliability and validation measurements. Skeletal Radiol 37:313–319, 2008
Pohl KM, Fisher J, Grimson WE, Kikinis R, Wells WM: A Bayesian model for joint segmentation and registration. Neuroimage 31:228–239, 2006
Pohl KM, Fisher J, Grimson WE, Wells WM: An expectation maximization approach for integrated registration, segmentation, and intensity correction. AI Memo 2005-010:1–13, 2005
Pohl KM, Fisher J, Kikinis R, Grimson WE, Wells WM: Shape based segmentation of anatomical structures in magnetic resonance images. Lect Notes Comput Sci 3765:489–498, 2005
Pohl KM, Fisher J, Levitt JJ, et al: A unifying approach to registration, segmentation, and intensity correction. Med Image Comput Comput Assist Interv Int Conf Med Image Comput Comput Assist Interv 8:310–318, 2005
Magnotta VA, Harris G, Andreasen NC, O'Leary DS, Yuh WT, Heckel D: Structural MR image processing using the BRAINS2 toolbox. Comput Med Imaging Graph 26:251–264, 2002
DeVries NA, Gassman EE, Kallemeyn NA, Shivanna KH, Magnotta VA, Grosland NM: Validation of phalanx bone three-dimensional surface segmentation from computed tomography images using laser scanning. Skeletal Radiol 37:35–42, 2008
Davis MH, Khotanzad A, Flamig DP, Harms SE: A physics-based coordinate transformation for 3-D image matching. IEEE Trans Med Imaging 16:317–328, 1997
Thirion JP: Image matching as a diffusion process: an analogy with Maxwell's demons. Med Image Anal 2:243–260, 1998
Donahue TL, Hull ML, Rashid MM, Jacobs CR: A finite element model of the human knee joint for the study of tibio-femoral contact. J Biomech Eng 124:273–280, 2002
Powell S, Magnotta VA, Johnson H, Jammalamadaka VK, Pierson R, Andreasen NC: Registration and machine learning-based automated segmentation of subcortical and cerebellar brain structures. Neuroimage 39:238–247, 2008
Sharp GC, Lee SW, Wehe DK: Invariant features and the registration of rigid bodies. In: Proc. IEEE Int. Conf. on Robotics and Autom. 1999:932–937, 1999
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The authors acknowledge the assistance of K. Pohl and gratefully acknowledge the financial support provided by the NIH Awards R21EB001501 and R01EB005973 and The University of Iowa Carver College of Medicine.
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Ramme, A.J., DeVries, N., Kallemyn, N.A. et al. Semi-automated Phalanx Bone Segmentation Using the Expectation Maximization Algorithm. J Digit Imaging 22, 483–491 (2009). https://doi.org/10.1007/s10278-008-9151-y
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DOI: https://doi.org/10.1007/s10278-008-9151-y