Automatic SWI Venography Segmentation Using Conditional Random Fields

IEEE Transactions on Medical Imaging

Volumn 34, Issue 12, 2015, Pages 2478-2491

  Bériault, Silvain ^a   Xiao, Yiming ^a   Collins, D Louis ^a   Pike, G Bruce ^b  

^a MCGILL UNIVERSITY   (Canada)

^b UNIVERSITY OF CALGARY   (Canada)

Author keywords

Conditional random fields; deep brain stimulation; image guided neurosurgery; MR venography; susceptibility weighted imaging

Indexed keywords

MATRIX ALGEBRA; NEUROSURGERY; SURGERY;

CONDITIONAL RANDOM FIELD; DEEP BRAIN STIMULATION; IMAGE GUIDED NEURO SURGERIES; MR VENOGRAPHY; SUSCEPTIBILITY WEIGHTED IMAGING;

RANDOM PROCESSES;

BRAIN DEPTH STIMULATION; HUMAN; MODEL; PARENCHYMA; PHLEBOGRAPHY; POPULATION MODEL; SURGERY; SUSCEPTIBILITY WEIGHTED IMAGING; VASCULARIZATION; VENOUS BLOOD; ADULT; AGED; ANATOMY AND HISTOLOGY; BRAIN CIRCULATION; BRAIN VEIN; COMPUTER ASSISTED SURGERY; FEMALE; IMAGE PROCESSING; MALE; MIDDLE AGED; NUCLEAR MAGNETIC RESONANCE IMAGING; PHYSIOLOGY; PROCEDURES;

ADULT; AGED; CEREBRAL VEINS; CEREBROVASCULAR CIRCULATION; DEEP BRAIN STIMULATION; FEMALE; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; MAGNETIC RESONANCE IMAGING; MALE; MIDDLE AGED; PHLEBOGRAPHY; SURGERY, COMPUTER-ASSISTED;

EID: 84960106354     PISSN: 02780062     EISSN: 1558254X     Source Type: Journal    
DOI: 10.1109/TMI.2015.2442236     Document Type: Article

References (51)

1. E. M. Haacke et al., "Susceptibility weighted imaging (SWI)," Magn. Reson. Med., vol. 52, no. 3, pp. 612-8, Sep. 2004.
2. E. M. Haacke et al., "Susceptibility-weighted imaging: Technical aspects and clinical applications, Part 1," AJNR Am. J. Neuroradiol., vol. 30, no. 1, pp. 19-30, Jan. 2009.
3. S. Bériault et al., "Neuronavigation using susceptibility-weighted venography: Application to deep brain stimulation and comparison with gadolinium contrast," J. Neurosurg., vol. 121, no. 1, pp. 131-41, 2014.
4. D. Lesage et al., "A review of 3D vessel lumen segmentation techniques: Models, features and extraction schemes," Med. Image Anal., vol. 13, no. 6, pp. 819-45, Dec. 2009.
5. C. Kirbas and F. Quek, "A review of vessel extraction techniques and algorithms," ACM Comput. Surv., vol. 36, no. 2, pp. 81-121, 2004.
6. D. L. Wilson and J. A. Noble, "An adaptive segmentation algorithm for time-of-flight MRA data," IEEE Trans.Med. Imag., vol. 18, no. 10, pp. 938-45, Oct. 1999.
7. M. S. Hassouna et al., "Cerebrovascular segmentation from TOF using stochastic models," Med. Image Anal., vol. 10, no. 1, pp. 2-18, Feb. 2006.
8. J. T. Hao, M. L. Li, and F. L. Tang, "Adaptive segmentation of cerebrovascular tree in time-of-flight magnetic resonance angiography," Med. Biol. Eng. Comput., vol. 46, no. 1, pp. 75-83, Jan. 2008.
9. S. Zhou et al., "Segmentation of brain magnetic resonance angiography images based on MAP-MRF with multi-pattern neighborhood system and approximation of regularization coefficient," Med. Image Anal., vol. 17, no. 8, pp. 1220-35, Dec. 2013.
10. M. S. Hassouna et al., "Statistical cerebrovascular segmentation for phase-contrast MRA data," in Proc. Int. Conf. Biomed. Eng., 2002, pp. 101-105.
11. M. Freiman et al., "Carotid vasculature modeling from patient CT angiography studies for interventional procedures simulation," Int. J. Comput. Assist. Radiol. Surg., vol. 7, no. 5, pp. 799-812, Sep. 2012.
12. C. Lorenz et al., "Multi-scale line segmentation with automatic estimation of width, contrast and tangential direction in 2D and 3D MEDICAL images," in CVRMed-MRCAS 1997, J. Troccaz, E. Grimson, and R. Mösges, Eds. Berlin, Germany: Springer, 1997, LNCS, pp. 233-242.
13. Y. Sato et al., "Three-dimensional multi-scale line filter for segmentation and visualization of curvilinear structures in medical images," Med. Image Anal., vol. 2, no. 2, pp. 143-68, Jun. 1998.
14. A. Frangi et al., "Multiscale vessel enhancement filtering," in MICCAI 1998, W. M. Wells, A. C. F. Colchester, and S. L. Delp, Eds. Berlin, Germany: Springer, 1998, LNCS, pp. 130-137.
15. K. Krissian et al., "Model-based detection of tubular structures in 3D images," Comput. Vis. Image Understand., vol. 80, no. 2, pp. 130-171, 2000.
16. A. Vasilevskiy and K. Siddiqi, "Flux maximizing geometric flows," IEEE Trans. Pattern Anal. Mach. Intell., vol. 24, no. 12, pp. 1565-1578, Dec. 2002.
17. M. W. Law and A. C. Chung, "Efficient implementation for spherical flux computation and its application to vascular segmentation," IEEE Trans. Image Process., vol. 18, no. 3, pp. 596-612, Mar. 2009.
18. M. K. Law and A. S. Chung, "Three dimensional curvilinear structure detection using optimally oriented flux," in Comput. Vision-ECCV 2008, D. Forsyth, P. Torr, and A. Zisserman, Eds. Berlin, Germany: Springer, 2008, LNCS, pp. 368-382.
19. S. J. Osher and J. A. Sethian, "Fronts propagating with curvature dependent speed: Algorithms based on Hamilton-Jacobi formulations," J. Comput. Phys., vol. 79, pp. 12-49, 1988.
20. L. M. Lorigo et al., "CURVES: Curve evolution for vessel segmentation," Med. Image Anal., vol. 5, no. 3, pp. 195-206, Sep. 2001.
21. N. D. Forkert et al., "3D cerebrovascular segmentation combining fuzzy vessel enhancement and level-sets with anisotropic energy weights," Magn. Reson. Imag., vol. 31, no. 2, pp. 262-71, Feb. 2013.
22. S. Beriault et al., "Automatic trajectory planning of DBS neurosurgery from multi-modal MRI datasets," in MICCAI 2011, G. Fichtinger, A. L. Martel, and T. M. Peters, Eds. Berlin, Germany: Springer, 2011, LNCS, pp. 259-66.
23. S. Beriault et al., "A multi-modal approach to computer-assisted deep brain stimulation trajectory planning," Int. J. Comput. Assist. Radiol. Surg., vol. 7, no. 5, pp. 687-704, Sep. 2012.
24. S. Barnes, M. Barth, and P. Koopmans, "Automatic vein segmentation and lesion detection: From SWI-MIPs to MR venograms," in Susceptibility Weighted Imaging in MRI. New York: Wiley, 2011, pp. 605-618.
25. P. J. Koopmans et al., "MR venography of the human brain using susceptibility weighted imaging at very high field strength,"MAGMA, vol. 21, no. 1-2, pp. 149-58, Mar. 2008.
26. N. Vigneau-Roy et al., "Regional variations in vascular density correlate with resting-state and task-evoked blood oxygen level-dependent signal amplitude," Hum. BrainMapp., vol. 35, no. 5, pp. 1906-20, May 2014.
27. S. Z. Li, Markov Random Field Modeling in Image Analysis. New York: Springer, 2009.
28. S. Bériault et al., "Automatic Markov random field segmentation of susceptibility-weighted MR venography," in CLIP 2013, M. Erdt, M. G. Linguraru, and C. O. Laura, Eds. et al. Berlin, Germany: Springer, 2014, LNCS, pp. 39-47.
29. Z. Karimaghaloo et al., "Hierarchical Conditional random fields for detection of gad-enhancing lesions in multiple sclerosis," in MICCAI 2012, Part II, N. Ayache, H. Delingette, and P. Golland, Eds. et al. Berlin, Germany: Springer, 2012, LNCS, pp. 379-86.
30. Z. Karimaghaloo et al., "Automatic detection of gadolinium-enhancing multiple sclerosis lesions in brain MRI using Conditional random fields," IEEE Trans. Med. Imag., vol. 31, no. 6, pp. 1181-94, Jun. 2012.
31. J. Besag, "On the statistical-analysis of dirty pictures," J. R. Stat. Soc. Ser. B-Methodol., vol. 48, no. 3, pp. 259-302, 1986.
32. Y. Boykov and V. Kolmogorov, "An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision," IEEE Trans. Pattern Anal. Mach. Intell., vol. 26, no. 9, pp. 1124-1137, Sep. 2004.
33. Y. Boykov, O. Veksler, and R. Zabih, "Fast approximate energy minimization via graph cuts," IEEE Trans. Pattern Anal. Mach. Intell., vol. 23, no. 11, pp. 1222-1239, Nov. 2001.
34. A. P. Dempster, N. M. Laird, and D. B. Rubin, "Maximum likelihood from incomplete data via the EM algorithm," J. R. Stat. Soc. Ser. B (Methodol.), vol. 39, no. 1, pp. 1-38, 1977.
35. L. Antiga, "Generalizing vesselness with respect to dimensionality and shape," Insight J., 2007.
36. D. Nain, A. Yezzi, and G. Turk, "Vessel segmentation using a shape driven flow," in Medical Image Computing and Comput.-Assisted Intervention-MICCAI 2004, C. Barillot, D. Haynor, and P. Hellier, Eds. Berlin, Germany: Springer, 2004, LNCS, pp. 51-59.
37. C. Denk and A. Rauscher, "Susceptibility weighted imaging with multiple echoes," J. Magn. Reson. Imag., vol. 31, no. 1, pp. 185-91, Jan. 2010.
38. J. G. Sled, A. P. Zijdenbos, and A. C. Evans, "A nonparametric method for automatic correction of intensity nonuniformity in MRI data," IEEE Trans. Med. Imag., vol. 17, no. 1, pp. 87-97, Feb. 1998.
39. L. G. Nyul, J. K. Udupa, and Z. Xuan, "New variants of a method of MRI scale standardization," IEEE Trans. Med. Imag., vol. 19, no. 2, pp. 143-150, Feb. 2000.
40. S. F. Eskildsen et al., "BEaST: Brain extraction based on nonlocal segmentation technique," Neuroimage, vol. 59, no. 3, pp. 2362-73, Feb. 2012.
41. P. Coupe et al., "An optimized blockwise nonlocal means denoising filter for 3-D magnetic resonance images," IEEE Trans. Med. Imag., vol. 27, no. 4, pp. 425-41, Apr. 2008.
42. N. Wiest-Daesslé et al., "Rician noise removal by non-local means filtering for low signal-to-Noise ratio MRI: Applications to DT-MRI," in MICCAI 2008, D. Metaxas, L. Axel, and G. Fichtinger, Eds. et al. Berlin, Germany: Springer, 2008, LNCS, pp. 171-179.
43. W. L. Nowinski et al., "A 3D model of human cerebrovasculature derived from 3T magnetic resonance angiography," in Neuroinformatics. New York: Springer, 2009, vol. 7, pp. 23-36.
44. L. R. Dice, "Measures of the amount of ecologic association between species," Ecology, vol. 26, no. 3, pp. 297-302, 1945.
45. M. Descoteaux, D. L. Collins, and K. Siddiqi, "A geometric flow for segmenting vasculature in proton-density weighted MRI," Med. Image Anal., vol. 12, no. 4, pp. 497-513, Aug. 2008.
46. C. Sutton and A. Mccallum, "Piecewise training for structured prediction," Mach. Learn., vol. 77, no. 2-3, pp. 165-194, 2009.
47. Y. Rubner, C. Tomasi, and L. J. Guibas, "A metric for distributions with applications to image databases," in Proc. 6th Int. Conf. Comput. Vis., 1998, pp. 59-66.
48. P. A. Yushkevich et al., "User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability," Neuroimage, vol. 31, no. 3, pp. 1116-1128, Jul. 2006.
49. B. Aubert-Broche et al., "Twenty new digital brain phantoms for creation of validation image data bases," IEEE Trans. Med. Imag., vol. 25, no. 11, pp. 1410-6, Nov. 2006.
50. V. Fonov et al., "Unbiased average ageappropriate atlases for pediatric studies," Neuroimage, vol. 54, no. 1, pp. 313-27, Jan. 2011.
51. D. L. Collins et al., "Automatic 3D model-based neuroanatomical segmentation," Human Brain Mapp., vol. 3, pp. 190-208, 1995.