A homotopy-based sparse representation for fast and accurate shape prior modeling in liver surgical planning

Medical Image Analysis

Volumn 19, Issue 1, 2015, Pages 176-186

  Wang, Guotai ^a   Zhang, Shaoting ^b   Xie, Hongzhi ^c   Metaxas, Dimitris N ^d   Gu, Lixu ^a  

^a SHANGHAI JIAO TONG UNIVERSITY   (China)

^b University of North Carolina at Charlotte   (United States)

^c PEKING UNION MEDICAL COLLEGE HOSPITAL   (China)

^d RUTGERS UNIVERSITY   (United States)

Author keywords

Fast optimization; Scalability; Segmentation; Shape prior; Sparse shape composition

Indexed keywords

EFFICIENCY; IMAGE SEGMENTATION; PROBLEM SOLVING; SCALABILITY; SHAPE OPTIMIZATION; SURGERY;

FAST OPTIMIZATIONS; INTERIOR POINT METHODS; LIVER SEGMENTATION; OPTIMIZATION PROBLEMS; PARAMETRIC DISTRIBUTIONS; SHAPE PRIORS; SIMILARITY COEFFICIENTS; SPARSE REPRESENTATION;

COMPUTATIONAL EFFICIENCY;

ACCURACY; ALGORITHM; ARTICLE; IMAGE ANALYSIS; IMAGE RECONSTRUCTION; LIVER SURGERY; LIVER SURGICAL PLANNING; PRODUCTIVITY; SPARSE SHAPE COMPOSITION; STATISTICAL MODEL; TREATMENT PLANNING; AUTOMATED PATTERN RECOGNITION; COMPUTER ASSISTED DIAGNOSIS; COMPUTER ASSISTED TOMOGRAPHY; COMPUTER SIMULATION; HUMAN; IMAGE QUALITY; IMAGE SUBTRACTION; LIVER NEOPLASMS; PREOPERATIVE CARE; PROCEDURES; RADIOGRAPHY; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY; THREE DIMENSIONAL IMAGING;

ALGORITHMS; COMPUTER SIMULATION; HUMANS; IMAGING, THREE-DIMENSIONAL; LIVER NEOPLASMS; MODELS, STATISTICAL; PATTERN RECOGNITION, AUTOMATED; PREOPERATIVE CARE; RADIOGRAPHIC IMAGE ENHANCEMENT; RADIOGRAPHIC IMAGE INTERPRETATION, COMPUTER-ASSISTED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; SUBTRACTION TECHNIQUE; TOMOGRAPHY, X-RAY COMPUTED;

EID: 84910065199     PISSN: 13618415     EISSN: 13618423     Source Type: Journal    
DOI: 10.1016/j.media.2014.10.003     Document Type: Article

References (44)

1. Afonso M.V., Bioucas-Dias J.M., Figueiredo M.A. An augmented lagrangian approach to the constrained optimization formulation of imaging inverse problems. IEEE Trans. Image Process. 2011, 20:681-695.
2. Asif, M.S., Romberg, J., 2013. Sparse recovery of streaming signals using l1-homotopy. CoRR abs/1306.3331.
3. Beck A., Teboulle M. A fast iterative shrinkage-thresholding algorithm for linear inverse problems. SIAM J. Imag. Sci. 2009, 2:183-202.
4. Broelsch C.E., Malago M., Testa G., Gamazo C.V. Living donor liver transplantation in adults: outcome in europe. Liver Transplant. 2000, 6:s64-s65.
5. Chambolle A., Pock T. A first-order primal-dual algorithm for convex problems with applications to imaging. J. Math. Imag. Vis. 2011, 40:120-145.
6. Chartrand R., Wotao Y. Iteratively reweighted algorithms for compressive sensing. IEEE International Conference on Acoustics, Speech and Signal Processing, 2008 (ICASSP 2008) 2008, 3869-3872. IEEE.
7. Chen J.G., Zhang S.W. Liver cancer epidemic in china: past, present and future. Seminars in Cancer Biology 2011, 59-69. Elsevier.
8. Cootes T.F., Taylor C.J. A mixture model for representing shape variation. Image Vis. Comput. 1999, 17:567-573.
9. Cootes T.F., Taylor C.J., Cooper D.H., Graham J. Active shape models-their training and application. Comput. Vis. Image Underst. 1995, 61:38-59.
10. Cremers D., Rousson M., Deriche R. A review of statistical approaches to level set segmentation: integrating color, texture, motion and shape. Int. J. Comput. Vis. 2007, 72:195-215.
11. Daubechies I., Defrise M., De Mol C. An iterative thresholding algorithm for linear inverse problems with a sparsity constraint. Commun. Pure Appl. Math. 2004, 57:1413-1457.
12. Davatzikos C., Tao X., Shen D. Hierarchical active shape models, using the wavelet transform. IEEE Trans. Med. Imag. 2003, 22:414-423.
13. Donoho D.L., Tsaig Y. Fast solution of l1-norm minimization problems when the solution may be sparse. IEEE Trans. Inform. Theory 2008, 54:4789-4812.
14. Etyngier P., Segonne F., Keriven R. Shape priors using manifold learning techniques. IEEE 11th International Conference on Computer Vision, 2007 (ICCV 2007) 2007, 1-8. IEEE.
15. Foucart S., Rauhut H. A Mathematical Introduction to Compressive Sensing 2013, Springer, New York, NY, USA.
16. Gao Y., Liao S., Shen D. Prostate segmentation by sparse representation based classification. Med. Phys. 2012, 39:6372-6387.
17. Ghose S., Oliver A., Mart R., Llad X., Freixenet J., Mitra J., Vilanova J., Comet-Batlle J., Meriaudeau F. Statistical shape and texture model of quadrature phase information for prostate segmentation. Int. J. Comput. Assist. Radiol. Surg. 2012, 7:43-55.
18. Hamarneh G., Li X. Watershed segmentation using prior shape and appearance knowledge. Image Vis. Comput. 2009, 27:56-98.
19. Heimann T., van Ginneken B., Styner M.A., Arzhaeva Y., Aurich V., Bauer C., Beck A., Becker C., Beichel R., Bekes G. Comparison and evaluation of methods for liver segmentation from ct datasets. IEEE Trans. Med. Imag. 2009, 28:1251-1265.
20. Heimann T., Meinzer H.P. Statistical shape models for 3d medical image segmentation: a review. Med. Image Anal. 2009, 13:543-563.
21. Heimann T., Wolf I., Meinzer H.P. Active shape models for a fully automated 3d segmentation of the liver. An evaluation on clinical data. Medical Image Computing and Computer-Assisted Intervention. (MICCAI 2006) 2006, 41-48. Springer.
22. Leventon M.E., Grimson W.E.L., Faugeras O. Statistical shape influence in geodesic active contours. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2000 2000, 316-323. IEEE.
23. Malioutov D.M., Cetin M., Willsky A.S. Homotopy continuation for sparse signal representation. Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005 (ICASSP'05) 2005, vol. 5:v/733-v/736. IEEE.
24. Nesterov, Y., 1983. A method of solving a convex programming problem with convergence rate o (1/k2). In: Soviet Mathematics Doklady, pp. 372-376.
25. Osborne M.R., Presnell B., Turlach B.A. A new approach to variable selection in least squares problems. IMA J. Numer. Anal. 2000, 20:389-403.
26. Rousson M., Paragios N. Shape priors for level set representations. Computer Vision-ECCV 2002 2000, 78-92. Springer.
27. Shen D., Herskovits E.H., Davatzikos C. An adaptive-focus statistical shape model for segmentation and shape modeling of 3-d brain structures. IEEE Trans. Med. Imag. 2001, 20:257-270.
28. Shi, F., Wang, L., Wu, G., Li, G., Gilmore, J.H., Lin, W., Shen, D., 2014. Neonatal atlas construction using sparse representation. Human brain mapping.
29. Shi Y., Qi F., Xue Z., Chen L., Ito K., Matsuo H., Shen D. Segmenting lung fields in serial chest radiographs using both population-based and patient-specific shape statistics. IEEE Trans. Med. Imag. 2008, 27:481-494.
30. Sotiropoulos G.C., Drhe N., Sgourakis G., Molmenti E.P., Beckebaum S., Baba H.A., Antoch G., Hilgard P., Radtke A., Saner F.H. Liver transplantation, liver resection, and transarterial chemoembolization for hepatocellular carcinoma in cirrhosis: which is the best oncological approach?. Digest. Diseases Sci. 2009, 54:2264-2273.
31. Trzasko J., Manduca A. Highly undersampled magnetic resonance image reconstruction via homotopic-minimization. IEEE Trans. Med. Imag. 2009, 28:106-121.
32. Vu N., Manjunath B.S. Shape prior segmentation of multiple objects with graph cuts. IEEE Conference on Computer Vision and Pattern Recognition, 2008 (CVPR 2008) 2008, 1-8. IEEE.
33. Wang, G., Zhang, S., Gu, L., 2014. Scalable sparse shape composition and its application to liver surgical planning. In: 2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI), pp. 377-380.
34. Wang G., Zhang S., Li F., Gu L. A new segmentation framework based on sparse shape composition in liver surgery planning system. Med. Phys. 2013, 40:051913.
35. Wright J., Ma Y., Mairal J., Sapiro G., Huang T.S., Yan S. Sparse representation for computer vision and pattern recognition. Proc. IEEE 2010, 98:1031-1044.
36. Xie J., Jiang Y., Tsui H.t. Segmentation of kidney from ultrasound images based on texture and shape priors. IEEE Trans. Med. Imag. 2005, 24:45-57.
37. Yan P., Xu S., Turkbey B., Kruecker J. Discrete deformable model guided by partial active shape model for trus image segmentation. IEEE Trans. Biomed. Eng. 2010, 57:1158-1166.
38. Yan P., Xu S., Turkbey B., Kruecker J. Adaptively learning local shape statistics for prostate segmentation in ultrasound. IEEE Trans. Biomed. Eng. 2011, 58:633-641.
39. Yang A.Y., Sastry S.S., Ganesh A., Ma Y. Fast l1-minimization algorithms and an application in robust face recognition: a review. 17th IEEE International Conference on Image Processing (ICIP), 2010 2010, 1849-1852. IEEE.
40. Yu Y., Zhang S., Huang J., Metaxas D., Axel L. Sparse deformable models with application to cardiac motion analysis. Lecture Notes in Computer Science 2013, vol. 79:208-219. Springer, Berlin Heidelberg. J. Gee, S. Joshi, K. Pohl, W. Wells, L. Zllei (Eds.).
41. Zhang S., Zhan Y., Dewan M., Huang J., Metaxas D.N., Zhou X.S. Towards robust and effective shape modeling: sparse shape composition. Med. Image Anal. 2012, 16:265-277.
42. Zhang S., Zhan Y., Metaxas D.N. Deformable segmentation via sparse representation and dictionary learning. Med. Image Anal. 2012, 16:1385-1396.
43. Zhang W., Yan P., Li X. Estimating patient-specific shape prior for medical image segmentation. International Symposium on Biomedical Imaging 2011, 1451-1454. IEEE.
44. Zhu, Y., Papademetris, X., Sinusas, A.J., Duncan, J.S., 2009. A dynamical shape prior for lv segmentation from rt3d echocardiography. In: Medical Image Computing and Computer-assisted Intervention. (MICCAI 2009), pp. 206-213.