Reconstruction of anatomical structures using statistical shape modeling
Smelkina N.A., Kosarev R.N., Nikonorov A.V., Bairikov I.M., Ryabov K.N., Avdeev A.V., Kazanskiy N.L.

 

Samara National Research University, Samara, Russia,
MedX, Samara, Russia,
Image Processing Systems Institute оf RAS – Branch of the FSRC “Crystallography and Photonics” RAS, Samara, Russia,

Samara State Medical University, Samara, Russia

Full text of article: Russian language.

Abstract:
We propose a method of statistical shape modeling applied to reconstructing anatomical structures with deformations. This method is promising for modeling deformed body parts that have a certain normal shape. The method of statistical shape modeling allows one to reconstruct a deformed object using information about the normal shape of the body part and its undeformed fragment, while taking into account peculiar individual features and the variability relative to the average shape.

Keywords:
statistical modeling, shape model, poin tset registration.

Citation:
Smelkina NA, Kosarev RN, Nikonorov AV, Bairikov IM, Ryabov KN, Avdeev AV, Kazanskiy NL. Reconstruction of anatomical structures using statistical shape modeling. Computer Optics 2017; 41(6): 897-904. DOI: 10.18287/2412-6179-2017-41-6-897-904.

References:

  1. Lüthi M, Blanc R, Albrecht T, Gass T, Goksel O, Büchler P, Kistler M, Bousleiman H, Reyes M, Cattin P, Vetter T. Statismo – A framework for PCA based statistical models. The Insight Journal 2012. Source: <http://www.insight-ournal.org/browse/publication/871>.
  2. Statismo. Source: <https://github.com/statismo/statismo>.
  3. Heimann T, Oguz I, Wolf I, Styner M, Meinzer HP. Implementing the automatic generation of 3D statistical shape models with ITK. The Insight Journal 2006. Source: <http://www.insight-journal.org/browse/publication/111>.
  4. Ross JC, Kindlmann GL, Okajima Y, Hatabu H, Díaz AA, Silverman EK, Washko GR, Dy J, San José Estépar R. Pulmonary lobe segmentation based on ridge surface sampling and shape model fitting. Med Phys 2013; 40(12): 121903. DOI: 10.1118/1.4828782.
  5. Heimann T, Wolf I, Meinze HP. Optimal landmark distributions for statistical shape model construction. Proc SPIE 2006; 6144: 61441J. DOI: 10.1117/12.653294.
  6. Soliman A, Khalifa F, Elnakib A, Abou El-Ghar M, Dunlap N, Wang B, Gimel'farb G, Keynton R, El-Baz A. Accurate lungs segmentation on CT chest images by adaptive appearance-guided shape modeling. IEEE Trans Med Imaging 2017; 36(1): 263-276. DOI: 10.1109/TMI.2016.2606370.
  7. Birkbeck N, Sofka M, Kohlberger T, Zhang J, Wetzl J, Kaftan J, Zhou SK. Robust segmentation of challenging lungs in CT using multi-stage learning and level set optimization. In Book: Suzuki K, ed. Computational intelligence in biomedical imaging. New York: Springer; 2014: 185-208. DOI: 10.1007/978-1-4614-7245-2_8.
  8. Jolliffe IT. Principal component analysis. 2nd ed. New York: Springer; 2002. ISBN: 0-387-95442-2.
  9. sMedX. StatisticalShapeModeling. Source: <https://github.com/sMedX/StatisticalShapeModeling>.
  10. Malladi R, Sethian JA, Vemuri B. Shape modeling with front propagation: A level set approach. IEEE Trans on Pattern Analysis and Machine Intelligence 1995; 17(2): 158-175. DOI: 10.1109/34.368173.
  11. Tustison NJ, Siqueira M, Gee JC. ND linear time exact signed Euclidean distance transform. The Insight Journal 2006. Source: <http://www.insight-journal.org/browse/pub­lication/77>.
  12. Maurer CR, Qi R, Raghavan V. A linear time algorithm for computing exact Euclidean distance transforms of binary images in arbitrary dimensions. IEEE Trans on Pattern Analysis and Machine Intelligence 2003; 25(2): 265-270. DOI: 10.1109/TPAMI.2003.1177156.
  13. Osher S, Fedkiw R. Level set methods and dynamic implicit surfaces. New York: Springer Science & Business Media; 2006.  ISBN: 978-0-387-95482-0.
  14. Bonnans JF, Gilbert GC, Lemarechal C, Sagastizabal CA. Numerical optimization: Theoretical and practical aspects. 2nd ed. Berlin, Heidelberg, New York: Springer-Verlag; 2006. ISBN: 978-3-540-35445-1.
  15. Styner MA, Rajamani KT, Nolte L-P, Zsemlye G, Székely G, Taylor ChJ, Davies RH. Evaluation of 3D correspondence methods for model building. In Book: Taylor C, Noble JA, eds. Biennial international conference on information processing in medical imaging. – Berlin, Heidelberg: Springer; 2003. DOI: 10.1007/978-3-540-45087-0_6.
  16. Besl PJ, McKay ND. Method for registration of 3-D shapes. In Book: Robotics-DL tentative. International Society for Optics and Photonics. 1992: 586-606.
  17. Chen Y, Medioni G. Object modelling by registration of multiple range images. Image and vision computing 1992; 10(3): 145-155. DOI: 10.1016/0262-8856(92)90066-C.
  18. Jian B, Vemuri BC. Robust point set registration using gaussian mixture models. IEEE Transactions on Pattern Analysis and Machine Intelligence 2011; 33(8): 1633-1645. DOI: 10.1109/TPAMI.2010.223.

© 2009, IPSI RAS
Institution of Russian Academy of Sciences, Image Processing Systems Institute of RAS, Russia, 443001, Samara, Molodogvardeyskaya Street 151; e-mail: ko@smr.ru; Phones: +7 (846 2) 332-56-22, Fax: +7 (846 2) 332-56-20