Academic Radiology
Volume 15, Issue 11 , Pages 1390-1403 , November 2008

Automated Segmentation of the Liver from 3D CT Images Using Probabilistic Atlas and Multilevel Statistical Shape Model

  • Toshiyuki Okada, MS

      Affiliations

    • Department of Computer Science, Osaka University Graduate School of Information Science and Technology, D11, 2-2 Yamadaoka, Suita, Osaka, 565-0871, Japan
    • Division of Image Analysis, Department of Medical Engineering, Osaka University Graduate School of Medicine, Suita, Osaka, Japan
  • ,
  • Ryuji Shimada, BS

      Affiliations

    • Department of Computer Science, Osaka University Graduate School of Information Science and Technology, D11, 2-2 Yamadaoka, Suita, Osaka, 565-0871, Japan
  • ,
  • Masatoshi Hori, MD, PhD

      Affiliations

    • Department of Radiology, Osaka University Graduate School of Medicine, Suita, Osaka, Japan
  • ,
  • Masahiko Nakamoto, PhD

      Affiliations

    • Department of Computer Science, Osaka University Graduate School of Information Science and Technology, D11, 2-2 Yamadaoka, Suita, Osaka, 565-0871, Japan
    • Division of Image Analysis, Department of Medical Engineering, Osaka University Graduate School of Medicine, Suita, Osaka, Japan
  • ,
  • Yen-Wei Chen, PhD

      Affiliations

    • College of Information Science and Engineering, Ritsumeikan University, Kutsatsu, Shiga, Japan
  • ,
  • Hironobu Nakamura, MD, PhD

      Affiliations

    • Department of Radiology, Osaka University Graduate School of Medicine, Suita, Osaka, Japan
  • ,
  • Yoshinobu Sato, PhD

      Affiliations

    • Department of Computer Science, Osaka University Graduate School of Information Science and Technology, D11, 2-2 Yamadaoka, Suita, Osaka, 565-0871, Japan
    • Division of Image Analysis, Department of Medical Engineering, Osaka University Graduate School of Medicine, Suita, Osaka, Japan
    • Corresponding Author InformationAddress correspondence to: Y.S.

Received 17 March 2008 ,Accepted 9 July 2008.

References 

  1. Cootes TF, Taylor CJ, Cooper DH, et al. Active shape models—their training and application. Comp Vision Image Understanding. 1995;61:38–59
  2. Leventon ME, Grimson WEL, Faugeras O. Statistical shape influence in geodesic active contours. Proc IEEE Computer Soc Conf Computer Vision Pattern Recognition. 2000;1:316–323
  3. Bailleul J, Ruan S, Constans JM. Statistical shape model-based segmentation of brain MRI images. 29th Annual International Conference of IEEE-EMBS, Engineering in Medicine and Biology Society, No. 4353527 In: Lyon, France: Cité Internationale; 2007;p. 5255–5258
  4. Shen D, Herskovits EH, Davatzikos C. An adaptive-focus statistical shape model for segmentation and shape modeling of 3-D brain. IEEE Trans Med Imaging. 2001;20:257–270
  5. Park H, Bland PH, Meyer CR. Construction of an abdominal probabilistic atlas and its application in segmentation. IEEE Trans Med Imaging. 2003;22:483–492
  6. Joshi S, Davis B, Jomier M, et al. Unbiased diffeomorphic atlas construction for computational anatomy. NeuroImage. 2004;23:S151–S160
  7. Straka M, Cruz AL, Dimitrov L, et al. Bone segmentation in CT-angiography data using a probabilistic atlas. Vision Modeling Visualization. 2003;505–512
  8. Lamecker H, Lange T, Seebaβ M. Segmentation of the liver using a 3D statistical shape model. Berlin: Technical report, Zuse Institue; 2004;
  9. Heimann T, Wolf I, Meinzer HP. Active shape models for a fully automated 3D segmentation of the liver— an evaluation on clinical data. 2006;Lect Notes Computer Sci 4191 (Proc MICCAI 2006, Part II), Copenhagen, Denmark; 41–48.
  10. Davatzikos C, Tao X, Shen D. Hierarchical active shape models, using the wavelet transform. IEEE Trans Med Imaging. 2003;22:414–423
  11. Zhao Z, Aylward SR, Teoh EK. A novel 3D partitioned active shape model for segmentation of brain MR images. 2005;Lect Notes Computer Sci; 3749 (Proc MICCAI 2005, Part I), Palm Springs, CA, 221–228.
  12. Yokota K, Okada T, Nakamoto M, et al. Construction of conditional statistical atlases of the liver based on spatial normalization using surrounding structures. 2006;Proc Computer Assisted Radiol Surg (CARS 2006), Osaka, Japan; 39–40.
  13. Zhou X, Kitagawa T, Hara T, et al. Constructing a probabilistic model for automated liver region segmentation using non-contrast x-ray torso CT images. 2006;Lect Notes Computer Sci; 4191 (Proc MICCAI 2006, Part II), Copenhagen, Denmark, 856–863.
  14. Okada T, Shimada R, Sato Y, et al. Automated segmentation of the liver from 3D CT images using probabilistic atlas and multi-level statistical shape model. 2007;Lect Notes Computer Sci; 4791 (Proc MICCAI 2007, Part I), Brisbane, Australia:86–93.
  15. Chui H, Rangarajan A. A new point matching algorithm for non-rigid registration. Computer Visi Image Understanding. 2003;89:114–141
  16. Besl PJ, Birch JB, Watson LT. Robust window operators. Machine Vis Appl. 1989;2:179–191
  17. Garland M, Heckbert P. Surface simplification using quadric error metrics. SIGGRAPH '97. 1997;209–216
  18. Heimann T, van Ginneken B, Styner M. MICCAI workshop on 3D segmentation in the clinic. http://mbi.dkfz-heidelberg.de/grand-challenge2007/Accessed March 17, 2008.
  19. Okada T, Yokota K, Nakamoto M, et al. Multi-level statistical shape model using shape stabilization terms for generic modeling of organs. Trans Inst Electron Inform Commun Eng D J91-D. 2008;(7):1862–1873[in Japanese].
  20. Couinaud C. The paracaval segments of the liver. J Hepato Bil Pancr Surg. 1994;1:145–151

PII: S1076-6332(08)00397-8

doi: 10.1016/j.acra.2008.07.008

Academic Radiology
Volume 15, Issue 11 , Pages 1390-1403 , November 2008