A segmentation framework for abdominal organs from CT scans

Artificial Intelligence in Medicine

Volumn 50, Issue 1, 2010, Pages 3-11

  Campadelli, Paola ^a   Casiraghi, Elena ^a   Pratissoli, Stella ^a  

^a UNIVERSITY OF MILAN   (Italy)

Author keywords

Abdominal organs; Computed tomography; Fast marching techniques; Pattern recognition; Virtual surgery applications

Indexed keywords

DIAGNOSIS; IMAGE SEGMENTATION; MEDICAL IMAGING; PATTERN RECOGNITION; STATISTICAL TESTS; VOLUME RENDERING;

ABDOMINAL ORGANS; ACTIVE CONTOUR MODEL; AUTOMATIC SEGMENTATIONS; COMPUTED TOMOGRAPHY IMAGES; FAST MARCHING METHODS; FAST-MARCHING; SPLEEN SEGMENTATIONS; VIRTUAL SURGERY;

COMPUTERIZED TOMOGRAPHY;

ABDOMINAL RADIOGRAPHY; ANATOMY; ARTICLE; COMPUTER ASSISTED TOMOGRAPHY; DIAGNOSTIC IMAGING; IMAGE ANALYSIS; IMAGE PROCESSING; LIVER; MATHEMATICAL COMPUTING; PRIORITY JOURNAL; SPLEEN;

EID: 77955276402     PISSN: 09333657     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.artmed.2010.04.010     Document Type: Article

References (47)

1. Shin H, Chung B, Lee S, Kim H, Ha WJ, Yang C. Measurement of kidney volume with multi-detector computed tomography scanning in young korean. Yonsei Medical Journal 2009:262-5, (published online, PMCID: 2679189). doi:10.3349/ymj.2009.50.2.262.
2. Nakayama Y., Li Q., Katsuragawa S., Ikeda R., Hiai Y., Awai K. Automated hepatic volumetry for living related liver transplantation at multisection CT. Radiology 2006, 240(3):743-748.
3. Lee C-C., Chung P-C., Chen Y-J. Classification of liver diseases from CT images using BP-CMAC neural network. Proceedings of 9th international workshop on cellular neural networks and their applications 2005, 118-121. Z. Chen (Ed.).
4. Werkgartner G., Lemmerer M., Hauser H., Sorantin E., Beichel R., Reitinger B. Augmented-reality-based liver-surgical planning system. European Surgery 2004, 36(5):270-274.
5. Harms J., Bartels M., Bourquain H., Peitgen H., Schulz T., Kahn T. Computerized CT-based 3D visualization technique in living related liver transplantation. Transplantation proceedings 2005, 1059-1062. B. Kahan (Ed.).
6. Fan S-T., Lo C-M., Liu C-L., Yong B-H., Chan J.K.-F., Ng I-L. Safety of donors in live donor liver transplantation using right lobe grafts. Archives of Surgery 2000, 135:336-340.
7. Kobashi M., Shapiro L.G. Knowledge-based organ identification from CT images. Pattern Recognition 1995, 28(4):475-491.
8. Schenk A., Prause G., Peitgen H. Efficient semiautomatic segmentation of 3D objects in medical images. Proceedings of medical image computing and computer-assisted intervention 2000, 186-195. S. Delp, A. DiGioia, B. Jaramaz (Eds.).
9. Beichel R., Bauer C., Bornik A., Sorantin E., Bischof H. Liver segmentation in CT data: A segmentation refinement approach. 3D segmentation in the clinic: a grand challenge, LNCS 2007, 235-245. Springer Berlin, Heidelberg. T. Heimann, M. Styner, B. van Ginneken (Eds.).
10. Beck A., Aurich V. Hepatux-a semiautomatic liver segmentation system. 3D segmentation in the clinic: a grand challenge, LNCS 2007, 225-233. Springer Berlin, Heidelberg. T. Heimann, M. Styner, B. van Ginneken (Eds.).
11. Bae K.T., Giger M.L., Chen C.T., Kahn C.E. Automatic segmentation of liver structure in CT images. Medical Physics 1993, 20:71-78.
12. Liu F., Zhao B., Kijewskic P., Wang L., Schwartz L. Liver segmentation for CT images using GVF snake. Medical Physics 2005, 32(12):3699-3706.
13. Lim S-J., Jeong Y-Y., Ho Y-S. Automatic liver segmentation for volume measurement in CT images. Journal of Visual Communication and Image Representation 2006, 17(4):860-875.
14. Lee J., Kim N., Lee H., Seo J., Won H., Shin Y. Efficient liver segmentation exploiting level-set speed images with 2. 5D shape propagation. 3D segmentation in the clinic: a grand challenge, LNCS 2007, 189-196. Springer Berlin, Heidelberg. T. Heimann, M. Styner, B. van Ginneken (Eds.).
15. Ruskò L., Bekes G., Nèmeth G., Fidrich M. Fully automatic liver segmentation for contrast-enhanced CT images. 3D segmentation in the clinic: a grand challenge, LNCS 2007, 143-150. Springer Berlin, Heidelberg. T. Heimann, M. Styner, B. van Ginneken (Eds.).
16. van Rikxoort E., Arzhaeva Y., van Ginneken B. Automatic segmentation of the liver in computed tomography scans with voxel classification and atlas matching. 3D segmentation in the clinic: a grand challenge, LNCS 2007, 101-108. Springer Berlin, Heidelberg. T. Heimann, M. Styner, B. van Ginneken (Eds.).
17. Lee C-C., Chung P-C., Tsai H-M. identifying multiple abdominal organs from CT image series using a multimodule contextual neural network and spatial fuzzy rules. IEEE Transaction on Information Technology in Biomedicine 2003, 7:208-217.
18. Lamecker H, Lange T, Seebass M. Segmentation of the liver using a 3D statistical shape model. ZIB-Report 04-09 04-09; 2004. p. 1-25.
19. Soler L., Delingette H., Malandain G., Montagnat J., Ayache N., Koehl C. Fully automatic anatomical, pathological, and functional segmentation from CT scans for hepatic surgery. Computer Aided Surgery 2001, 6(3):131-142.
20. Kainmuller D., Lange T., Lamecker H. Shape constrained automatic segmentation of the liver based on a heuristic intensity model. 3D segmentation in the clinic: a grand challenge, LNCS 2007, 109-116. Springer Berlin, Heidelberg. T. Heimann, M. Styner, B. van Ginneken (Eds.).
21. Saddi K., Rousson M., Chefd'hotel C., Cheriet F. Global-to-local shape matching for liver segmentation in CT imaging. 3D segmentation in the clinic: a grand challenge, LNCS 2007, 207-214. Springer Berlin, Heidelberg. T. Heimann, M. Styner, B. van Ginneken (Eds.).
22. Heimann T., Meinzer H., Wolf I. A statistical deformable model for the segmentation of liver CT volumes. 3D segmentation in the clinic: a grand challenge, LNCS 2007, 161-166. Springer Berlin, Heidelberg. T. Heimann, M. Styner, B. van Ginneken (Eds.).
23. Park H., Bland P., Meyer C. Construction of an abdominal probabilistic atlas and its application in segmentation. IEEE Transactions on Medical Imaging 2003, 22(4):483-492.
24. Shimizu A., Ohno R., Ikegami T., Kobatake H., Nawano S., Smutek D. Multi-organ segmentation in three dimensional abdominal CT images. International Journal of Computer Assisted Radiology and Surgery (CARS) 2006, 1(7):76-78.
25. Furukawa D., Shimizu A., Kobatake H. Automatic liver segmentation method based on maximum a posterior probability estimation and level set method. 3D segmentation in the clinic: a grand challenge, LNCS 2007, 117-124. Springer Berlin, Heidelberg. T. Heimann, M. Styner, B. van Ginneken (Eds.).
26. Pan S., Dawant B. Automatic 3D segmentation of the liver from abdominal CT images: a level-set approach. Proceedings of SPIE medical imaging 2001, vol. 4322, LNCS 2001, 128-138. M. Sonka, K.M. Hanson (Eds.).
27. Dawant B., Li R., Lennon B., Li S. Semi-automatic segmentation of the liver and its evaluation on the miccai 2007 grand challenge data set. 3D segmentation in the clinic: a grand challenge, LNCS 2007, 215-221. Springer Berlin, Heidelberg. T. Heimann, M. Styner, B. van Ginneken (Eds.).
28. Campadelli P., Casiraghi E., Esposito A. Liver segmentation from computed tomography scans: a survey and a new algorithm. Artificial Intelligence in Medicine 2008, 45:185-196.
29. Ginneken B., Heimann T., Stiner M. 3D segmentation in the clinic: a grand challenge. 3D segmentation in the clinic: a grand challenge, LNCS 2007, 7-15. Springer Berlin, Heidelberg. T. Heimann, M. Styner, B. van Ginneken (Eds.).
30. Niessen W., Bouma C., Vincken K., Viergever M. Error metrics for quantitative evaluation of medical image segmentation. Proceedings of the theoretical foundations of computer vision, TFCV on performance characterization in computer vision 2000, 275-284. B. Deventer (Ed.).
31. Gerig G., Jomier M., Chakos M. Valmet: a new validation tool for assessing and improving 3d object segmentation. Proceedings of medical image computing and computer assisted intervention, LNCS 2001, 516-523. W. Niessen, M. Viergever (Eds.).
32. Seghers D, Slagmolen P, Lambelin Y, Hermans J, Loeckx D, Maes F, et al. Landmark based liver segmentation using local shape and local intensity models. In: Heimann T, Styner M, van Ginneken B, editors. 3D segmentation in the clinic: a grand challenge, LNCS. Heidelberg: Springer Berlin; 2007. p. 135-42.
33. Wimmer A., Soza G., Hornegger J. Two-stage semi-automatic organ segmentation framework using radial basis functions and level sets. 3D segmentation in the clinic: a grand challenge, LNCS 2007, 207-214. Springer Berlin, Heidelberg. T. Heimann, M. Styner, B. van Ginneken (Eds.).
34. Slagmolen P., Elen A., Seghers D., Loeckx D., Maes F., Haustermans K. Atlas based liver segmentation using nonrigid registration with a b-spline transformation model. 3D segmentation in the clinic: a grand challenge, LNCS 2007, 197-206. Springer Berlin, Heidelberg. T. Heimann, M. Styner, B. van Ginneken (Eds.).
35. Schmidt G., Athelogou M., Schonmeyer R., Korn R., Binnig G. Cognition network technology for a fully automated 3d segmentation of liver. 3D segmentation in the clinic: a grand challenge, LNCS 2007, 125-133. Springer Berlin, Heidelberg. T. Heimann, M. Styner, B. van Ginneken (Eds.).
36. Susomboon R., Stan Raicu D., Furst J. A hybrid approach for liver segmentation. 3D segmentation in the clinic: a grand challenge, LNCS 2007, 151-160. Springer Berlin, Heidelberg. T. Heimann, M. Styner, B. van Ginneken (Eds.).
37. Chi Y., Cashman P., Bello F., Kitney R. A discussion on the evaluation of a new automatic liver volume segmentation method for specified CT image datasets. 3D segmentation in the clinic: a grand challenge, LNCS 2007, 167-175. Springer Berlin, Heidelberg. T. Heimann, M. Styner, B. van Ginneken (Eds.).
38. Malladi R., Sethian J., Vemuri B. Shape modeling with front propagation: a level set approach. IEEE Transactions on Pattern Analysis and Machine Intelligence 1995, 17(2):158-175.
39. Sethian J.A. Level set methods and fast marching methods 1999, Cambridge University Press, Cambridge, England.
40. Sethian J., Vladimirsky A. Ordered upwind methods for static hamilton-jacobi equations: theory and algorithm. SIAM Journal on Numerical Analysis 2003, 41(1):325-363.
41. Malladi R., Sethian J., Vemuri B. Shape modeling with front propagation: a level set approach. IEEE Transactions on Pattern Analysis and Machine Intelligence 1995, 17:158-174.
42. Jackson S., Thomas R. Cross-sectional imaging made easy 2004, Elsevier Health Sciences, Oxford, England.
43. Perona P., Malik J. Scale-space and edge detection using anisotropic diffusion. IEEE Transactions on Pattern Analysis and Machine Intelligence 1990, 12(7):629-639.
44. Weickert J. A review of nonlinear diffusion filtering. Scale-Space Theories in Computer Vision 1997, 3-28.
45. Campadelli P., Casiraghi E., Pratissoli S. Fully automatic segmentation of abdominal organs from CT images using fast marching methods. Proceedings of the 21st IEEE international symposium on computer-based medical systems (CBMS), Jyvaskyla 2008, 554-559. S. Puuronen, M. Pechenizkiy, A. Tsymbal, D. Lee (Eds.).
46. Kass M., Witkin A., Terzopoulos D. Snakes: active contour models. International Journal of Computer Vision 1998, 321-331.
47. McInerney T., Terzopoulos D. Topology adaptive deformable surfaces for medical image volume segmentation. IEEE Transactions on Medical Imaging 1999, 18(10):840-850.