DOI QR코드

DOI QR Code

Automatic Segmentation of Renal Parenchyma using Graph-cuts with Shape Constraint based on Multi-probabilistic Atlas in Abdominal CT Images

복부 컴퓨터 단층촬영영상에서 다중 확률 아틀라스 기반 형상제한 그래프-컷을 사용한 신실질 자동 분할

  • Lee, Jaeseon (Department of Software Convergence, Seoul Women's University) ;
  • Hong, Helen (Department of Software Convergence, Seoul Women's University) ;
  • Rha, Koon Ho (Department of Urology, Yonsei University College of Medicine)
  • 이재선 (서울여자대학교 소프트웨어융합학과) ;
  • 홍헬렌 (서울여자대학교 소프트웨어융합학과) ;
  • 나군호 (연세대학교 의과대학 비뇨기과)
  • Received : 2016.07.07
  • Accepted : 2016.08.31
  • Published : 2016.09.01

Abstract

In this paper, we propose an automatic segmentation method of renal parenchyma on abdominal CT image using graph-cuts with shape constraint based on multi-probabilistic atlas. The proposed method consists of following three steps. First, to use the various shape information of renal parenchyma, multi-probabilistic atlas is generated by cortex-based similarity registration. Second, initial seeds for graph-cuts are extracted by maximum a posteriori (MAP) estimation and renal parenchyma is segmented by graph-cuts with shape constraint. Third, to reduce alignment error of probabilistic atlas and increase segmentation accuracy, registration and segmentation are iteratively performed. To evaluate the performance of proposed method, qualitative and quantitative evaluation are performed. Experimental results show that the proposed method avoids a leakage into neighbor regions with similar intensity of renal parenchyma and shows improved segmentation accuracy.

본 논문에서는 복부 CT 영상에서 다중 확률 아틀라스 기반 형상제한 그래프-컷을 사용한 신실질 자동 분할 방법을 제안한다. 제안 방법은 다음의 세 단계로 구성된다. 첫째, 신실질의 다양한 형상정보를 이용하기 위해 피질기반 유사정합을 통한 다중 확률 아틀라스를 생성한다. 둘째, 최대사후확률 추정을 통해 그래프-컷의 초기 씨앗을 추출하고, 형상제한 그래프-컷을 통해 신실질을 분할한다. 셋째, 확률 아틀라스의 정합 오차를 줄이고 분할 정확도를 높이기 위해, 정합 및 분할을 반복적으로 수행한다. 제안방법의 성능을 평가하기 위해 정성적 평가 및 정량적 평가를 수행하였다. 실험결과 제안방법이 신실질과 유사한 밝기값을 갖는 주변 영역으로의 누출을 방지하여 개선된 분할 정확도를 보여준다.

Keywords

Acknowledgement

Supported by : 한국연구재단

References

  1. C. Weight, B. Larson, A. Fergany, T. Gao, B. Lane, and C. Campbell, "Nephrectomy induced chronic renal insufficiency is associated with increased risk of cardiovascular death and death from any cause in patients with localized cTlb renal masses," The Journal of urology, vol.183, no.4, pp.1317-23, 2010. https://doi.org/10.1016/j.juro.2009.12.030
  2. M. Maddox, S. Mandava, J. Liu, A. Boonjindasup, and B. Lee, "Robotic partial nephrectomy for clinical stage Tlb Tumors: Intermediate oncologic and functional outcomes," Clinical genitourinary cancer, vol.13, no.1, pp.94-99, 2014. https://doi.org/10.1016/j.clgc.2014.07.011
  3. D. Kim, Y. Jang, J. Lee, and H. Hong et aI., "Two-year analysis for predicting renal function and contralateral hypertrophy after robot-assisted partial nephrectomy: A three dimensional segmentation teclmology study," International Journal of Urology, Vol.22, pp. 1115-1111, 2015.
  4. http://www.kestrelstudio.comlportfolio/medical-illustrationlanat mmcal/kidney-sinus.php
  5. G. Yan and B. Wang, "An automatic kidney segmentation from abdominal CT images," Proc. of 2010 IEEE International Conference on Intelligent Computing and Intelligent Systems, vol.1, pp.280-284, 2010.
  6. G. Yan and B. Wang, "Automatic segmentation of kidney without using contrast medium on abdominal CT images," Proc. of 2008 3rd Int. Conference on Intelligent System and Knowledge Engineering, vol.1, pp.1242-1246, 2008.
  7. D. Lin, C. Lei, and S. Hung, "Computer-aided kidney segmentation on abdominal CT Images," IEEE Transactions on Information Technology in Biomedicine, vol.10, no. 1, pp.59-65, 2006. https://doi.org/10.1109/TITB.2005.855561
  8. G. Dai, Z. Li, 1. Gu, L. Wang, X. Li, and Y. Xie, "Segmentation of kidneys from 3D fast growcut algorithrn," Proc. of 2013 IEEE International Conference on Image Processing, pp.1144-1147, 2013.
  9. B. Tsagaan, A. Shimizu, H. Kobatake, K Miyakawa, and Y. Hanzawa, "Segmentation of kidney by using a deformable model,"Proc. of 2001 IEEE Internaltional Conference on Image Processing, vo1.3, pp. 1059-1062, 2001.
  10. Y. Huang, P. Chllllg, C. Huang, and C. Huang, "Multiphase level set with multi dynamic shape models on kidney segmentation of CT image," Proc. of 2009 IEEE Biomedical Circuits and Systems Conference, pp.141-144, 2009.
  11. F. Khalifa, A. Elnakib, G. Beache, and G. Gimel'farb, et al., "3D kidney segmentation level set approach guided by a novel stochastic speed function," Proc. of MICCAI 2011, Part III. LNCS, vol.6893, pp. 587-594,2011.
  12. F. Khalifa, G. Gimel'farb, and M. Abo et al., "A new deformable model-based segmentation approach for accurate extraction of the kidney from abdominal CT images," Proc. of 2011 IEEE International Conference on Image Processing, pp. 3393-3396, 2011.
  13. A. Ali, A. Farag, and A. El-Baz "Graph cut framework for kidney segmentation with prior shape constraints," Proc. of MICCAI 2007, Part I. LNCS, vol. 4791, pp.384-392, 2007.
  14. M. Freiman, A. Kronman, S. Esses, L. loskowicz, and J. Sosna, "Non-parametric iterative model constraint graph min-cut for automatic kidney segmentation," Proc. of MICCAI 2010, Part III. LNCS, vol.6363, pp.73-80, 2010.
  15. H. Jo, and H. Hong, "Automatic nodule segmentation using intensity, curvature and morphology information in lung CT images," Journal of KIISE : software and applications, vol.39, no.7, pp.537-546, 2012.
  16. M. Powell, "An efficient method for finding the minimum of a function of several variables without calculating derivatives ", The computer journal, vol.7, no.2, pp.155-162, 1964. https://doi.org/10.1093/comjnl/7.2.155
  17. A. Shimizu, R. Ohno, T. Ikegami, H. Kobatake, S.Nawano, and D. Smutek," Segmentation of multiple organs in non-contrast 3D abdominal CT Images," Int. J. Comput. Assist.Radiol. Surg. vol.2, pp.135-142, 2007. https://doi.org/10.1007/s11548-007-0135-z
  18. V. Kolmogorov and R. Zabih, " What energy functinos can be minimized via graph cuts?," Pattern Analysis and Machine Intelligence, vol. 26, no. 2, pp. 147-159, 2004. https://doi.org/10.1109/TPAMI.2004.1262177
  19. Y. Boykov and G. Funka-Lea, "Graph cuts and efficient N-D image segmentation," International journal of computer vision, vol. 70, no. 2, pp. 109-131,2006. https://doi.org/10.1007/s11263-006-7934-5