A generic, robust and fully-automatic workflow for 3D CT liver segmentation

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 8198 LNCS, Issue , 2013, Pages 241-250

  Gauriau, Romane ^a,b   Cuingnet, Rémi ^b   Prevost, Raphael ^b   Mory, Benoit ^b   Ardon, Roberto ^b   Lesage, David ^b   Bloch, Isabelle ^a  

^a INSTITUT TELECOM   (France)

^b PHILIPS RESEARCH LABORATORIES   (Germany)

Author keywords

3D CT; fully automatic segmentation; Liver segmentation; regression forest; template deformation

Indexed keywords

COMPUTATIONAL SPEED; FIELDS OF VIEWS; LIVER SEGMENTATION; MEAN DISTANCES; PROBABILITY MAPS; REFERENCE DATABASE; REGRESSION FORESTS; SURGERY PLANNING;

COMPUTERIZED TOMOGRAPHY; FORESTRY; THREE DIMENSIONAL;

DEFORMATION;

BIOMETRICS; DEFORMATION; IMAGE ANALYSIS; REGRESSION ANALYSIS; THREE DIMENSIONAL DESIGN;

EID: 84886496749     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-41083-3_27     Document Type: Conference Paper

References (19)

1. Murthy, R., Nunez, R., Szklaruk, J., Erwin, W., Madoff, D.C., Gupta, S., Ahrar, K., Wallace, M.J.: Yttrium-90 microsphere therapy for hepatic malignancy: Devices, indications, technical considerations, and potential complications. Radiographics 25(suppl. 1), 41-55 (2005)
2. Campadelli, P., Casiraghi, E., Esposito, A.: Liver segmentation from computed tomography scans: A survey and a new algorithm. Artificial Intelligence in Medicine 45(2-3), 185-196 (2009)
3. Mharib, A.M., Ramli, A.R., Mashohor, S., Mahmood, R.B.: Survey on liver CT image segmentation methods. Artificial Intelligence Review 37(2), 83-95 (2011)
4. Heimann, T., Styner, M., van Ginneken, B.: Sliver07 (2007), http://www.sliver07.org (accessed: May 12, 2013)
5. Heimann, T., et al.: Comparison and evaluation of methods for liver segmentation from CT datasets. IEEE Transactions on Medical Imaging 28(8), 1251-1265 (2009)
6. Ling, H., Zhou, S.K., Zheng, Y., Georgescu, B., Suehling, M., Comaniciu, D.: Hierarchical, learning-based automatic liver segmentation. In: Proc. CVPR 2008, pp. 1-8 (2008)
7. Zhang, X., Tian, J., Deng, K., Wu, Y., Li, X.: Automatic liver segmentation using a statistical shape model with optimal surface detection. IEEE Transactions on Biomedical Engineering 57(10), 2622-2626 (2010)
8. Linguraru, M.G., Sandberg, J.K., Li, Z., Shah, F., Summers, R.M.: Automated segmentation and quantification of liver and spleen from CT images using normalized probabilistic atlases and enhancement estimation. Medical Physics 37(2), 771 (2010)
9. Cuingnet, R., Prevost, R., Lesage, D., Cohen, L.D., Mory, B., Ardon, R.: Automatic detection and segmentation of kidneys in 3D CT images using random forests. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) MICCAI 2012, Part III. LNCS, vol. 7512, pp. 66-74. Springer, Heidelberg (2012)
10. Criminisi, A., Robertson, D., Konukoglu, E., Shotton, J., Pathak, S., White, S., Siddiqui, K.: Regression forests for efficient anatomy detection and localization in computed tomography scans. Medical Image Analysis (in press, 2013)
11. Mory, B., Somphone, O., Prevost, R., Ardon, R.: Real-time 3D image segmentation by user-constrained template deformation. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) MICCAI 2012, Part I. LNCS, vol. 7510, pp. 561-568. Springer, Heidelberg (2012)
12. Klein, S., Staring, M., Murphy, K., Viergever, M.A., Pluim, J.P.: Elastix: a toolbox for intensity-based medical image registration. IEEE Transactions on Medical Imaging 29, 196-205 (2010)
13. Fitzgibbon, A.W.: Robust registration of 2D and 3D point sets. Image and Vision Computing 21(13-14), 1145-1153 (2003)
14. Besl, P.J., McKay, N.D.: A method for registration of 3-D shapes. IEEE Transactions on Pattern Analaysis and Machine Intelligence 14(2), 239-256 (1992)
15. He, K., Sun, J., Tang, X.: Guided image filtering. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part I. LNCS, vol. 6311, pp. 1-14. Springer, Heidelberg (2010)
16. Kainmüller, D., Lange, T., Lamecker, H.: Shape constrained automatic segmentation of the liver based on a heuristic intensity model. In: Proc. MICCAI Workshop 3D Segmentation in the Clinic: A Grand Challenge, pp. 109-116 (2007)
17. Wimmer, A., Soza, G., Hornegger, J.: A generic probabilistic active shape model for organ segmentation. In: Yang, G.-Z., Hawkes, D., Rueckert, D., Noble, A., Taylor, C. (eds.) MICCAI 2009, Part II. LNCS, vol. 5762, pp. 26-33. Springer, Heidelberg (2009)
18. Linguraru, M.G., Richbourg, W.J., Watt, J.M., Pamulapati, V., Summers, R.M.: Liver and tumor segmentation and analysis from CT of diseased patients via a generic affine invariant shape parameterization and graph cuts. In: Yoshida, H., Sakas, G., Linguraru, M.G. (eds.) Abdominal Imaging 2011. LNCS, vol. 7029, pp. 198-206. Springer, Heidelberg (2012)
19. Huang, C., Jia, F., Li, Y., Zhang, X., Luo, H., Fang, C., Fan, Y.: Fully automatic liver segmentation using probability atlas registration. In: International Conference on Electronics, Communications and Control 2012, pp. 126-129 (2012)