Statistical 4D graphs for multi-organ abdominal segmentation from multiphase CT

Medical Image Analysis

Volumn 16, Issue 4, 2012, Pages 904-914

  Linguraru, Marius George ^a,b   Pura, John A ^a   Pamulapati, Vivek ^a   Summers, Ronald M ^a  

^a NATIONAL INSTITUTES OF HEALTH   (United States)

^b CHILDREN'S NATIONAL MEDICAL CENTER   (United States)

Author keywords

4D graph; Enhancement; Multi organ segmentation; Multiphase CT; Shape

Indexed keywords

4D GRAPH; ABDOMINAL ORGANS; AUTOMATED METHODS; COMPREHENSIVE ANALYSIS; CONTRAST-ENHANCED; CONTRAST-ENHANCED CT; CT DATA; GRAPH CUT; MEDICAL IMAGES; MULTIPHASE CT; ORGAN SEGMENTATION; PARZEN WINDOWS; POPULATION TRAINING; QUANTITATIVE MEASURES; SHAPE; SOFT TISSUE;

COMPUTER AIDED DIAGNOSIS; COMPUTERIZED TOMOGRAPHY; HOSPITAL DATA PROCESSING; IMAGE ENHANCEMENT; IMAGE SEGMENTATION; MAMMOGRAPHY; MEDICAL IMAGING; OPTIMIZATION; TISSUE;

POPULATION STATISTICS;

ABDOMEN; ARTICLE; COMPUTED TOMOGRAPHY SCANNER; COMPUTER ASSISTED DIAGNOSIS; COMPUTER ASSISTED TOMOGRAPHY; CONTRAST ENHANCEMENT; DIAGNOSTIC IMAGING; FOUR DIMENSIONAL IMAGING; HUMAN; KIDNEY; LIVER; MULTIPHASE COMPUTER ASSISTED TOMOGRAPHY; NORMAL HUMAN; PORTAL VEIN; PRIORITY JOURNAL; RADIODIAGNOSIS; SPLEEN; STATISTICAL ANALYSIS;

ALGORITHMS; DATA INTERPRETATION, STATISTICAL; HUMANS; IMAGING, THREE-DIMENSIONAL; PATTERN RECOGNITION, AUTOMATED; RADIOGRAPHIC IMAGE ENHANCEMENT; RADIOGRAPHIC IMAGE INTERPRETATION, COMPUTER-ASSISTED; RADIOGRAPHY, ABDOMINAL; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; TOMOGRAPHY, X-RAY COMPUTED; VISCERA;

EID: 84859420953     PISSN: 13618415     EISSN: 13618423     Source Type: Journal    
DOI: 10.1016/j.media.2012.02.001     Document Type: Article

References (62)

1. Al-Kofahi Y., Lassoued W., Lee W., Roysam B. Improved automatic detection and segmentation of cell nuclei in histopathology images. IEEE Trans. Biomed. Eng. 2009, 57(4):841-852.
2. Ali, A.M., Farag, A.A., El-Baz, A.S., 2007. Graph cuts for kidney segmentation with prior shape constraints. In: Proceedings of MICCAI 2007, Part I, LNCS, pp. 384-392.
3. Bae E., Tai X.C. Efficient global minimization for the multiphase Chan-Vese model of image segmentation, energy minimization methods in computer vision and pattern recognition. Lect. Notes Comput. Sci. 2009, 5681:28-41.
4. Bauer C., Pock T., Sorantin E., Bischof H., Beichel R. Segmentation of interwoven 3D tubular tree structures utilizing shape priors and graph cuts. Med. Image Anal. 2010, 14(2):172-184.
5. Ben Ayed I., Punithakumar K., Li S., Islam A., Chong J. Left ventricle segmentation via graph cut distribution matching. Med. Image Comput. Comput. Assist. Interven. 2009, 12(Pt 2):901-909.
6. Boykov, Y., Jolly, M.P., 2001. Interactive graph cuts for optimal boundary and region segmentation of objects in N-D images. In: Int. Conf. Comput. Vis., vol. I, pp. 105-112.
7. Boykov Y., Veksler O., Zabih R. Fast approximate energy minimization via graph cuts. IEEE Trans. Pattern Anal. Mach. Intell. 2001, 23(11):1222-1239.
8. Boykov, Y., Kolmogorov, V., 2003. Computing geodesics and minimal surfaces via graph cuts. In: Int. Conf. Comput. Vis.
9. Chen, J.H., Shapiro, L.G., 2008. Medical image segmentation via min s-t cuts with sides constraints. In: Int. Conf. Pattern Recog.
10. Cremers D., Osher S.J., Soatto S. Kernel density estimation and intrinsic alignment for shape priors in level set segmentation. Int. J. Comput. Vis. 2006, 69(3):335-351.
11. Danelson K.A., Stitzel J.D. Volumetric splenic injury measurement in ct scans for comparison with injury score. Biomed. Sci. Instrum. 2008, 44:159-164.
12. Das P., Veksler O., Zavadsky V., Boykov Y. Semiautomatic segmentation with compact shape priors. Image Vis. Comput. 2008, 27(1-2):206-219.
13. Deleus F., Van Hulle M.M. A connectivity-based method for defining regions-of-interest in fMRI data. IEEE Trans. Image Process. 2009, 18(8):1760-1771.
14. Delingette H., Ayache N. Hepatic surgery simulation. Commun. ACM 2005, 48(2):31-36.
15. Delong, A., Boykov, Y., 2009. Globally optimal segmentation of multi-region objects. In: International Conference on Computer Vision, pp. 285-292.
16. El-Zehiry N., Grady L. Discrete optimization of the multiphase piecewise constant Mumford-Shah functional, energy minimization methods in computer vision and pattern recognition. Lect. Notes Comput. Sci. 2011, 6819:233-246.
17. Esneault S., Lafon C., Dillenseger J.L. Liver vessels segmentation using a hybrid geometrical moments/graph cuts method. IEEE Trans. Biomed. Eng. 2010, 57(2):276-283.
18. Freedman, D., Zhang, T., 2005. Interactive graph cut based segmentation with shape priors. In: Computer Vision Pattern Recognition.
19. Freiman, M., Kronman, A., Esses, S.J., Joskowicz, L., Sosna, J., 2010. Non-parametric iterative model constraint graph min-cut for automatic kidney segmentation. In: MICCAI. LNCS 6363, pp. 73-80.
20. Fritz G.A., Schoellnast H., Deutschmann H.A., Quehenberger F., Tillich M. Multiphasic multidetector-row CT (MDCT) in detection and staging of transitional cell carcinomas of the upper urinary tract. Eur. Radiol. 2006, 16(6):1244-1252.
21. Gramfort A., Keriven R., Clerc M. Graph-based variability estimation in single-trial event-related neural responses. IEEE Trans. Biomed. Eng. 2010, 57(5):1051-1061.
22. García-Lorenzo D., Lecoeur J., Arnold D.L., Collins D.L., Barillot C. Multiple sclerosis lesion segmentation using an automatic multimodal graph cuts. Med. Image Comput. Comput. Assist. Interven. 2009, 12(Pt 2):584-591.
23. Grady, L., Jolly, M.P., 2008. Weights and topology: a study of the effects of graph construction on 3D image segmentation. In: MICCAI 2008, Part I, LNCS 5241, pp. 153-161.
24. Heimann T., et al. Comparison and evaluation of methods for liver segmentation from CT datasets. IEEE Trans. Med. Imaging 2009, 28(8):1251-1265.
25. Hu, X., Shimizu, A., Kobatake H., Nawano, S., 2004. Independent analysis of four-phase abdominal CT images. In: Proceedings of MICCAI 2004, LNCS 3217, pp. 916-924.
26. Khan S.M., Shah M. Tracking multiple occluding people by localizing on multiple scene planes. IEEE Trans. Pattern Anal. Mach. Intell. 2009, 31(3):505-519.
27. Kolmogorov, V., Boykov, Y., 2005. What metrics can be approximated by geo-cuts or global optimization of length/area and flux. In: Int. Conf. Comput. Vis.
28. van der Lijn F., den Heijer T., Breteler M.M., Niessen W.J. Hippocampus segmentation in MR images using atlas registration, voxel classification, and graph cuts. Neuroimage 2008, 43(4):708-720.
29. Lin X., Cowan B., Young A. Model-based graph cut method for segmentation of the left ventricle. Proc. IEEE Eng. Med. Biol. Soc. 2005, 3:3059-3062.
30. Linguraru, M.G., Summers, R.M., 2008. Multi-organ segmentation in 4D contrast-enhanced abdominal CT. In: IEEE Symposium on Biomedical Imaging, pp. 45-48.
31. Linguraru M.G., Sandberg J.A., Li Z., Shah F., Summers R.M. Atlas-based automated segmentation of spleen and liver using adaptive enhancement estimation. Med. Phys. 2010, 37(2):771-783.
32. Liu, L., Raber, D., et al., 2008. Interactive separation of segmented bones in CT volumes using graph cut. In: Proceedings of MICCAI 2008, Part I, LNCS 5241, pp. 296-304.
33. Lötjönen J.M., Wolz R., Koikkalainen J.R., Thurfjell L., Waldemar G., Soininen H., Rueckert D. Fast and robust multi-atlas segmentation of brain magnetic resonance images. Neuroimage 2010, 49(3):2352-2365.
34. Malcolm, J., Rathi, Y., Tannenbaum, A., 2007. Graph cut segmentation with nonlinear shape priors. In: Int. Conf. Image Process.
35. Montillo, A., Shotton, J., Winn, J., Iglesias, J.E., Metaxas, D., Criminisi, A., 2011. Entangled decision forests and their application for semantic segmentation of CT images. In: Information Processing in Medical Imaging. Lecture Notes in Computer Science, vol. 6801, pp. 184-196.
36. Okada T., Shimada R., Hori M., Nakamoto M., Chen Y.W., Nakamura H., Sato Y. Automatic segmentation of the liver from 3D CT images using probabilistic atlas and multilevel statistical shape model. Acad. Radiol. 2008, 15:1390-1403.
37. Okada, T., Yokota, K., Hori, M., Nakamoto, M., Nakamura, H., Sato, Y., 2008b. Construction of hierarchical multi-organ statistical atlases and their application to multi-organ segmentation from CT images. In: MICCAI 2008, pp. 502-509.
38. Park H., Bland P.H., Meyer C.R. Construction of an abdominal probabilistic atlas and its application in segmentation. IEEE Trans. Med. Imaging 2003, 22(4):483-492.
39. Parzen E. On estimation of a probability density function and mode. Ann. Math. Stat. 1962, 33:1065-1076.
40. Perona P., Malik J. Scale-space and edge detection using anisotropic diffusion. IEEE Trans. Pattern Anal. Mach. Intell. 1990, 12:629-639.
41. Reyes, M., Gonzalez Ballester, M.A., Li, Z., Kozic, N., Chin, S., Summers, R.M., Linguraru, M.G., 2009. Anatomical variability of organs via principal factor analysis from the construction of an abdominal probabilistic atlas. in: IEEE International Symposium on Biomedical Imaging (ISBI), pp. 682-685.
42. Sadananthan S.A., Zheng W., Chee M.W., Zagorodnov V. Skull stripping using graph cuts. Neuroimage 2010, 49(1):225-239.
43. Sakashita, M., Kitasaka, T., Mori, K., Suenaga, Y., Nawano, S., 2007. A method for extracting multi-organ from four-phase contrasted CT images based on CT value distribution estimation using EM-algorithm. In: SPIE, 6509, pp. 1C1-1C12.
44. Seabra J.C., Pedro L.M., Fernandes J.F., Sanches J.M. A 3-D ultrasound-based framework to characterize the echo morphology of carotid plaques. IEEE Trans. Biomed. Eng. 2009, 56(5):1442-1453.
45. Seifert, S., Barbu, A., Zhou, K., Liu, D., Feulner, J., Huber, M., Suehling, M., Cavallaro, A., Comaniciu, D., 2009. Hierarchical parsing and semantic navigation of full body CT data. In: Proc. SPIE, 7259, pp. 725902-725908.
46. Shim H., Chang S., Tao C., Wang J.H., Kaya D., Bae K.T. Semiautomated segmentation of kidney from high-resolution multidetector computed tomography images using a graph-cuts technique. J. Comput. Assist. Tomogr. 2009, 33(6):893-901.
47. Shimizu A., Ohno R., Ikegami T., Kobatake H., Nawano S., Smutek D. Segmentation of multiple organs in non-contrast 3D abdominal CT images. Int. J. Comput. Assist. Radiol. Surg. 2007, 2:135-142.
48. Shimizu A., Kimoto T., Kobatake H., Nawano S., Shinozaki K. Automated pancreas segmentation from three-dimensional contrast-enhanced computed tomography. Int. J. Comput. Assist. Radiol. Surg. 2010, 5:85-98.
49. Shimizu, A., Nakagomi, K., Narihira, T., Kobatake, H., Nawano, S., Shinozaki, K., Ishizu, K., Togashi K., 2010b. Automated segmentation of 3D CT Images based on Statistical Atlas and Graph Cuts. In: MICCAI-MCV, pp. 129-138.
50. Soler L., Delingette H., Malandain G., Montagnat J., Ayache N., Koehl C., Dourthe O., Malassagne B., Smith M., Mutter D., Marescaux J. Fully automatic anatomical, pathological, and functional segmentation from CT scans for hepatic surgery. Comput. Aided Surg. 2001, 6(3):131-142.
51. Song, Y., Bulpitt, A., Brodlie, K., 2009. Liver segmentation using automatically defined patient specific B-spline surface models. In: MICCAI 2009 Part II, LNCS 5762, pp. 43-50.
52. So, R.W.K., Chung, A.C., 2009. Multi-level non-rigid image registration using graph-cuts. In: IEEE Int. Conf. Acoustics, Speech and Signal Processing, pp. 397-400.
53. Spiegel M., Hahn D.A., Daum V., Wasza J., Hornegger J. Segmentation of kidneys using a new active shape model generation technique based on non-rigid image registration. Comput. Med. Imaging Graph. 2009, 33(1):29-39.
54. Studholme C., Hill D.L.G., Hawkes D.J. An overlap invariant entropy measure of 3D medical image alignment. Pattern Recogn. 1999, 32(1):71-86.
55. Thirion J.P. Image matching as a diffusion process: an analogy with Maxwell's demons. Med. Image Anal. 1998, 2(3):243-260.
56. Veksler, O., 2008. Star shape prior for graph-cut image segmentation. In: Eur. Conf. Comp. Vis.
57. Voci S.L., Gottlieb R.H., Fultz P.J., Mehta A., Parthasarathy R., Rubens D.J., Strang J.G. Delayed computed tomographic characterization of renal masses: preliminary experience. Abdom. Imaging 2000, 25(3):317-321.
58. Vu, N., Manjunath, B.S., 2008. Shape prior segmentation of multiple objects with graph cuts. In: Computer Vision Pattern Recognition.
59. Wels M., Carneiro G., Aplas A., Huber M., Hornegger J., Comaniciu D. A discriminative model-constrained graph cuts approach to fully automated pediatric brain tumor segmentation in 3-D MRI. Med. Image Comput. Comput. Assist. Interven. 2008, 11(Pt 1):67-75.
60. Wimmer A., Soza G., Hornegger J. A generic probabilistic active shape model for organ segmentation. Med. Image Comput. Comput. Assist. Interven. 2009, 12(Pt 2):26-33.
61. Zhan Y., Zhou X.S., Peng Z., Krishnan A. Active scheduling of organ detection and segmentation in whole-body medical images. Med. Image Comput. Comput. Assist. Interven. 2008, 11(Pt 1):313-321.
62. Zheng, Y., Baloch, S., Englande, S., Schnall, M.D., Shen, S., 2007. Segmentation and classification of breast tumor using dynamic contrast-enhanced MR images. In: MICCAI 2007, Part II, LNCS 4792, pp. 393-401.