M3VR—A multi-stage, multi-resolution, and multi-volumes-of-interest volume registration method applied to 3D endovaginal ultrasound

English version

Autoři: Qi Xing ^aff001; Parag Chitnis ^aff003; Siddhartha Sikdar ^aff003; Jonia Alshiek ^aff004; S. Abbas Shobeiri ^aff003; Qi Wei ^aff003
Působiště autorů: Department of Computer Science, George Mason University, Fairfax, Virginia, United States of America ^aff001; The School of Information Science and Technology, Southwest Jiaotong University, Sichuan, China ^aff002; Department of Bioengineering, George Mason University, Fairfax, Virginia, United States of America ^aff003; Department of Obstetrics & Gynecology, INOVA Health System, Falls Church, Virginia, United States of America ^aff004
Vyšlo v časopise: PLoS ONE 14(11)
Kategorie: Research Article
prolekare.web.journal.doi_sk: https://doi.org/10.1371/journal.pone.0224583

Souhrn

Heterogeneity of echo-texture and lack of sharply delineated tissue boundaries in diagnostic ultrasound images make three-dimensional (3D) registration challenging, especially when the volumes to be registered are considerably different due to local changes. We implemented a novel computational method that optimally registers volumetric ultrasound image data containing significant and local anatomical differences. It is A Multi-stage, Multi-resolution, and Multi-volumes-of-interest Volume Registration Method. A single region registration is optimized first for a close initial alignment to avoid convergence to a locally optimal solution. Multiple sub-volumes of interest can then be selected as target alignment regions to achieve confident consistency across the volume. Finally, a multi-resolution rigid registration is performed on these sub-volumes associated with different weights in the cost function. We applied the method on 3D endovaginal ultrasound image data acquired from patients during biopsy procedure of the pelvic floor muscle. Systematic assessment of our proposed method through cross validation demonstrated its accuracy and robustness. The algorithm can also be applied on medical imaging data of other modalities for which the traditional rigid registration methods would fail.

Klíčová slova:

Imaging techniques – Magnetic resonance imaging – Mathematical functions – Ultrasound imaging – Bone imaging – Biopsy – Musculoskeletal injury

Zdroje

1. Oliveira FPM, Tavares JMRS. Medical image registration: a review. Comput Method Biomec. 2014;17(2):73–93. doi: 10.1080/10255842.2012.670855

2. Rosenman JG, Miller EP, Tracton G, Cullip TJ. Image Registration: An Essential Part of Radiation Therapy Treatment Planning. Int J Radiat Oncol Biol Phys. 1998;40(1):197—205. doi: 10.1016/s0360-3016(97)00546-4 9422577

3. Bardera A, Feixas M, Boada I, Rigau J, Sbert M. Registration-Based Segmentation Using the Information Bottleneck Method. In: Pattern Recognition and Image Analysis. Springer, Berlin, Heidelberg; 2007. 130–137.

4. Sauer F. Image Registration: Enabling Technology for Image Guided Surgery and Therapy. In: In Proc. IEEE 27th Eng. Med. Biol. Soc. Shanghai, China; 2005. 7242–7245.

5. Gooya A, Biros G, Davatzikos C. An EM algorithm for brain tumor image registration: A tumor growth modeling based approach. In: In Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern. Recognit. San Francisco, CA; 2010. 39–46.

6. Maintz JBA, Viergever MA. In: An Overview of Medical Image Registration Methods. Oxford Univeristy Press; 1998.

7. Glocker B, Sotiras A, Komodakis N, Paragios N. Deformable Medical Image Registration: Setting the State of the Art with Discrete Methods. Annu Rev Biomed Eng. 2011;13(1):219–244. doi: 10.1146/annurev-bioeng-071910-124649 21568711

8. Ferrante E, Paragios N. Slice-to-volume medical image registration: A survey. Med Image Anal. 2017;39:101–123. doi: 10.1016/j.media.2017.04.010 28482198

9. Sotiras A, Davatzikos C, Paragios N. Deformable Medical Image Registration: A Survey. IEEE Trans Med Imaging. 2013;32(7):1153–1190. doi: 10.1109/TMI.2013.2265603 23739795

10. Klein S, Staring M, Murphy K, Viergever MA, Pluim JPW. elastix: A Toolbox for Intensity-Based Medical Image Registration. IEEE Trans Med Imaging. 2010;29(1):196–205. doi: 10.1109/TMI.2009.2035616 19923044

11. Shams R, Sadeghi P, Kennedy RA, Hartley RI. A Survey of Medical Image Registration on Multicore and the GPU. IEEE Signal Process Mag. 2010;27(2):50–60. doi: 10.1109/MSP.2009.935387

12. Loening AM, Gambhir SS. AMIDE: A Free Software Tool for Multimodality Medical Image Analysis. Mol Imaging. 2003;2(3):131–137. doi: 10.1162/15353500200303133 14649056

13. Che C, Mathai TS, Galeotti J. Ultrasound registration: A review. Methods. 2017;115:128–143. doi: 10.1016/j.ymeth.2016.12.006 27965119

14. Oelze ML, Mamou J. Review of Quantitative Ultrasound: Envelope Statistics and Backscatter Coefficient Imaging and Contributions to Diagnostic Ultrasound. IEEE Trans Ultrason Ferroelectr Freq Control. 2016;63(2):336–351. doi: 10.1109/TUFFC.2015.2513958 26761606

15. Oelze ML. Quantitative ultrasound techniques and improvements to diagnostic ultrasonic imaging. In: In Proc. IEEE Int. Ultrason. Symp. Dresden, Germany; 2012. 232–239.

16. Low G, Leen E. 10. In: Clinical Ultrasound (Third Edition). Edinburgh: Churchill Livingstone; 2011.

17. Smith LF, Henry-Tillman R, Harms S, Hronas T, Mancino AT, Westbrook KC, et al. Hematoma-Directed Ultrasound-Guided Breast Biopsy. Ann Surg. 2001;233(5):669–675. doi: 10.1097/00000658-200105000-00011 11323506

18. Mattes D, Haynor DR, Vesselle H, Lewellyn TK, Eubank W. Nonrigid multimodality image registration. vol. 4322; 2001. 1609–1620.

19. So RWK, Tang TWH, Chung ACS. Non-rigid image registration of brain magnetic resonance images using graph-cuts. Pattern Recognit. 2011;44(10):2450–2467. doi: 10.1016/j.patcog.2011.04.008

20. Woo J, Hong BW, Hu CH, Shung KK, Kuo CCJ, Slomka PJ. Non-Rigid Ultrasound Image Registration Based on Intensity and Local Phase Information. J Signal Process Syst. 2009;54(1-3):33–43. doi: 10.1007/s11265-008-0218-2

21. Schalk SG, Postema A, Saidov TA, Demi L, Smeenge M, de la Rosette JJMCH, et al. 3D surface-based registration of ultrasound and histology in prostate cancer imaging. Comput Med Imaging Graph. 2016;47:29–39. doi: 10.1016/j.compmedimag.2015.11.001 26647110

22. Shekhar R, Zagrodsky V, Garcia MJ, Thomas JD. Registration of real-time 3-D ultrasound images of the heart for novel 3-D stress echocardiography. IEEE Trans Med Imaging. 2004;23(9):1141–1149. doi: 10.1109/TMI.2004.830527 15377123

23. Francois R, Fablet R, Barillot C. Robust statistical registration of 3D ultrasound images using texture information. In: In Proc. Int. Conf. Image Process. vol. 1. Barcelona, Spain; 2003. I581–I584.

24. Hacihaliloghlu I, Rasoulian A, Rohling RN, Abolmaesumi P. Statistical shape model to 3D ultrasound registration for spine interventions using enhanced local phase features. Med Image Comput Comput Assist Interv. 2013;16(2):361–368. doi: 10.1007/978-3-642-40763-5_45 24579161

25. Banerjee J, Klink C, Peters ED, Niessen WJ, Moelker A, van Walsum T. Fast and robust 3D ultrasound registration—Block and game theoretic matching. Med Image Anal. 2015;20(1):173–183. doi: 10.1016/j.media.2014.11.004 25484018

26. Zhang W, Noble JA, Brady JM. Adaptive Non-rigid Registration of Real Time 3D Ultrasound to Cardiovascular MR Images. In: Inf. Process. Med. Imaging. Lecture Notes in Computer Science. Springer, Berlin, Heidelberg; 2007. 50–61.

27. Meyer CR, Boes JL, Kim B, Bland PH, Lecarpentier GL, Fowlkes JB, et al. Semiautomatic registration of volumetric ultrasound scans. Ultrasound Med Biol. 1999;25(3):339–347. doi: 10.1016/s0301-5629(98)00148-3 10374978

28. Panza JA. Real-time three-dimensional echocardiography: An overview. Int J Cardiovasc Imaging. 2001;17(3):227–235. doi: 10.1023/A:1010669009889 11587457

29. Shekhar R, Zagrodsky V. Mutual information-based rigid and nonrigid registration of ultrasound volumes. IEEE Trans Med Imaging. 2002;21(1):9–22. doi: 10.1109/42.981230 11838664

30. Papademetris X, Jackowski AP, Schultz RT, Staib LH, Duncan JS. Integrated Intensity and Point-Feature Nonrigid Registration. Med Image Comput Comput Assist Interv. 2001;3216(2004):763–770. 20473359

31. Abdel-Basset M, Fakhry AE, El-henawy I, Qiu T, Sangaiah AK. Feature and Intensity Based Medical Image Registration Using Particle Swarm Optimization. J Med Syst. 2017;41(12):197. doi: 10.1007/s10916-017-0846-9 29098445

32. Rister B, Horowitz MA, Rubin DL. Volumetric Image Registration From Invariant Keypoints. IEEE Trans Image Process. 2017;26(10):4900–4910. doi: 10.1109/TIP.2017.2722689 28682256

33. Schneider RJ, Perrin DP, Vasilyev NV, Marx GR, del Nido PJ, Howe RD. Real-time image-based rigid registration of three-dimensional ultrasound. Med Image Anal. 2012;16(2):402–414. doi: 10.1016/j.media.2011.10.004 22154960

34. Zikic D, Wein W, Khamene A, Clevert DA, Navab N. Fast Deformable Registration of 3D-Ultrasound Data Using a Variational Approach. In: In Proc. Med. Image Comput. Comput. Assist. Interv. Lecture Notes in Computer Science. Springer, Berlin, Heidelberg; 2006. 915–923.

35. Song H, Qiu P. A parametric intensity-based 3D image registration method for magnetic resonance imaging. Signal Image Video P. 2017;11(3):455–462. doi: 10.1007/s11760-016-0981-7

36. Zitová B, Flusser J. Image registration methods: a survey. Image Vis Comput. 2003;21(11):977–1000. doi: 10.1016/S0262-8856(03)00137-9

37. Crum WR, Hartkens T, Hill DLG. Non-rigid image registration: theory and practice. Br J Radiol. 2004;77(2):S140–S153. doi: 10.1259/bjr/25329214 15677356

38. Viergever MA, Maintz JBA, Klein S, Murphy K, Staring M, Pluim JPW. A survey of medical image registration—under review. Med Image Anal. 2016;33:140–144. doi: 10.1016/j.media.2016.06.030 27427472

39. DeLancey JOL. The hidden epidemic of pelvic floor dysfunction: Achievable goals for improved prevention and treatment. Am J Obstet Gynecol. 2005;192(5):1488–1495. doi: 10.1016/j.ajog.2005.02.028 15902147

40. Subak LL, Waetjen LE, van den Eeden S, Thom DH, Vittinghoff E, Brown JS. Cost of pelvic organ prolapse surgery in the United States. Obstet Gynecol. 2001;98(4):646–651. doi: 10.1016/s0029-7844(01)01472-7 11576582

41. DeLancey JOL, Morgan DM, Fenner DE, Kearney R, Guire K, Miller JM, et al. Comparison of levator ani muscle defects and function in women with and without pelvic organ prolapse. Obstet Gynecol. 2007;109(2 Pt 1):295–302. doi: 10.1097/01.AOG.0000250901.57095.ba 17267827

42. Shobeiri A, editor. Practical Pelvic Floor Ultrasonography: A Multicompartmental Approach to 2D/3D/4D Ultrasonography of the Pelvic Floor. Springer International Publishing; 2017.

43. Rostaminia G, White DE, Quiroz LH, Shobeiri SA. Levator plate descent correlates with levator ani muscle deficiency. Neurourol Urodyn. 2015;34(1):55–59. doi: 10.1002/nau.22509 24132730

44. Ashton-Miller JA, DeLANCEY JOL. Functional Anatomy of the Female Pelvic Floor. Ann N Y Acad Sci. 2007;1101(1):266–296. doi: 10.1196/annals.1389.034 17416924

45. DeLancey JOL, Kearney R, Chou Q, Speights S, Binno S. The appearance of levator ani muscle abnormalities in magnetic resonance images after vaginal delivery. Obstet Gynecol. 2003;101(1):46–53. doi: 10.1016/s0029-7844(02)02465-1 12517644

46. Santoro GA, Wieczorek AP, Dietz HP, Mellgren A, Sultan AH, Shobeiri SA, et al. State of the art: an integrated approach to pelvic floor ultrasonography. Ultrasound Obstet Gynecol. 2011;37(4):381–396. doi: 10.1002/uog.8816 20814874

47. Shobeiri SA, Santiago A. Use of Ultrasound Imaging in Pelvic Organ Prolapse: an Overview. Curr Obstet Gynecol Rep. 2015;4(2):109–114. doi: 10.1007/s13669-015-0117-z

48. Wei Q, Sikdar S, Chitnis P, Rostaminia G, Abbas Shobeiri S. In: Shobeiri SA, editor. Patient-Specific Studies of Pelvic Floor Biomechanics Using Imaging. Cham: Springer International Publishing; 2017. 337–344.

49. Javadian P, O’Leary D, Rostaminia G, North J, Wagner J, Quiroz LH, et al. How does 3D endovaginal ultrasound compare to magnetic resonance imaging in the evaluation of levator ani anatomy? Neurourol Urodyn. 2017;36(2):409–413. doi: 10.1002/nau.22944 26669505

50. Rostaminia G, Peck JD, Quiroz LH, Shobeiri SA. Characteristics associated with pelvic organ prolapse in women with significant levator ani muscle deficiency. Int Urogynecol J. 2016;27(2):261–267. doi: 10.1007/s00192-015-2827-1 26342811

51. Busacchi P, Giorgio RD, Santini D, Bellavia E, Perri T, Oliverio C, et al. A histological and immunohistochemical study of neuropeptide containing somatic nerves in the levator animuscle of women with genitourinary prolapse. Acta Obstet Gynecol Scand. 1999;78(1):2–5. doi: 10.1034/j.1600-0412.1999.780102.x 9926883

52. Cabrera J, Meer P. Unbiased estimation of ellipses by bootstrapping. IEEE Trans Pattern Anal Mach Intell. 1996;18(7):752–756. doi: 10.1109/34.506797

53. Hans MMMLl Johnson J. The ITK Software Guide: Introduction and Development Guidelines. Kitware Inc.; 2015.

54. Wang Z, Slabaugh G, Unal G, Fang T. Registration of ultrasound image using an information-theoretic feature detector. In: In Proc. 4th IEEE Int. Symp. Biomed. Imaging. Arlington, VA; 2007. 736–739.

55. Wang J, Horvath S, Stetten G, Siegel M, Galeotti J. Real-time registration of video with ultrasound using stereo disparity. In: In SPIE Med. imaging. San Diego, CA; 2012.

56. Narayanasamy G, LeCarpentier GL, Roubidoux M, Fowlkes JB, Schott AF, Carson PL. Spatial registration of temporally separated whole breast 3D ultrasound images. Med Phys. 2009;36(9):4288–4300. doi: 10.1118/1.3193678 19810503

57. Silva TD, Uneri A, Zhang X, Ketcha M, Han R, Jacobson M, et al. Real-time image-based 3D-2D registration for ultrasound-guided spinal interventions. In: In SPIE: Med. Imaging. Houston, Texas; 2018.

58. Dwith Chenna YN, Ghassemi P, Pfefer TJ, Casamento J, Wang Q. Free-Form Deformation Approach for Registration of Visible and Infrared Facial Images in Fever Screening. Sensors (Basel). 2018;18(1):E25.

59. Schneider CA, Rasband WS, Eliceiri KW. NIH Image to ImageJ: 25 years of image analysis. Nat Methods. 2012;9(7):671–675. doi: 10.1038/nmeth.2089 22930834

60. Modat M, Cash DM, Daga P, Winston GP, Duncan JS, Ourselin S. Global image registration using a symmetric block-matching approach. J Med Imaging (Bellingham). 2014;1(2).

61. Klein S, Staring M, Murphy K, Viergever MA, Pluim JPW. elastix: A Toolbox for Intensity-Based Medical Image Registration. IEEE Transactions on Medical Imaging. 2010;29(1):196–205. doi: 10.1109/TMI.2009.2035616 19923044

62. Berger M, Levine JA, Nonato LG, Taubin G, Silva CT. A Benchmark for Surface Reconstruction. ACM Trans Graph. 2013;32(2):20:1–20:17. doi: 10.1145/2451236.2451246

63. Ou Y, Sotiras A, Paragios N, Davatzikos C. DRAMMS: Deformable registration via attribute matching and mutual-saliency weighting. Med Image Anal. 2011;15(4):622–639. doi: 10.1016/j.media.2010.07.002 20688559

M3VR—A multi-stage, multi-resolution, and multi-volumes-of-interest volume registration method applied to 3D endovaginal ultrasound

Souhrn

Klíčová slova:

Zdroje

PLOS One

Aktuální možnosti diagnostiky a léčby litiáz