Context-aware anatomical landmark detection: Application to deformable model initialization in prostate CT images

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

Volumn 8679, Issue , 2014, Pages 165-173

  Gao, Yaozong ^a,b   Shen, Dinggang ^a  

^a UNIVERSITY OF NORTH CAROLINA SCHOOL OF MEDICINE   (United States)

^b University of North Carolina   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFICATION (OF INFORMATION); DEFORMATION; MEDICAL IMAGING; REGRESSION ANALYSIS; UROLOGY; ARTIFICIAL INTELLIGENCE; COMPUTER AIDED INSTRUCTION; COMPUTERIZED TOMOGRAPHY; LEARNING SYSTEMS;

3-D DISPLACEMENT; ANATOMICAL LANDMARKS; DEFORMABLE MODELING; DISPLACEMENT FIELD; DISPLACEMENT VECTORS; INITIAL DISPLACEMENTS; LANDMARK DETECTION; SPATIAL CONSISTENCY;

COMPUTERIZED TOMOGRAPHY; MEDICAL IMAGING;

EID: 84921669478     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-319-10581-9_21     Document Type: Article

References (13)

1. Johnson, H.J., Christensen, G.E.: Consistent landmark and intensity-based image registration. IEEE TMI 21(5), 450–461 (2002)
2. Zhang, S., Zhan, Y., Dewan, M., et al.: Towards robust and effective shape modeling: Sparse shape composition. Med. Imag. Anal. 16(1), 265–277 (2012)
3. Zheng, Y., Barbu, A., Georgescu, B., Scheuering, M., Comaniciu, D.: Four- Chamber Heart Modeling and Automatic Segmentation for 3-D Cardiac CT Volumes Using Marginal Space Learning and Steerable Features. IEEE TMI 27(11), 1668–1681 (2008)
4. Viola, P., Jones, M.J.: Robust Real-Time Face Detection. IJCV 57(2), 137–154 (2004)
5. Cheng, E., Chen, J., Yang, J., et al.: Automatic Dent-landmark detection in 3-D CBCT dental volumes. In: EMBC 2011, pp. 6204–6207 (2011)
6. Zhan, Y., et al.: Robust Automatic Knee MR Slice Positioning Through Redundant and Hierarchical Anatomy Detection. IEEE TMI 30(12), 2087–2100 (2011)
7. Criminisi, A., Shotton, J., Robertson, D., Konukoglu, E.: Regression Forests for Efficient Anatomy Detection and Localization in CT Studies. In: Menze, B., Langs, G., Tu, Z., Criminisi, A. (eds.) MICCAI 2010. LNCS, vol. 6533, pp. 106–117. Springer, Heidelberg (2011)
8. Cootes, T.F., Ionita, M.C., Lindner, C., Sauer, P.: Robust and Accurate Shape Model Fitting Using Random Forest Regression Voting. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part VII. LNCS, vol. 7578, pp. 278–291. Springer, Heidelberg (2012)
9. Tu, Z., Bai, X.: Auto-Context and Its Application to High-Level Vision Tasks and 3D Brain Image Segmentation. IEEE PAMI 32(10), 1744–1757 (2010)
10. Coup, P., et al.: Patch-based segmentation using expert priors: Application to hippocampus and ventricle segmentation. NeuroImage 54(2), 940–954 (2010)
11. Rousson, M., Khamene, A., Diallo, M., Celi, J.C., Sauer, F.:Constrained SurfaceEvolutions for Prostate and Bladder Segmentation inCTImages. In:Liu,Y., Jiang, T.-Z., Zhang, C. (eds.) CVBIA 2005. LNCS, vol. 3765, pp. 251–260. Springer, Heidelberg (2005)
12. Lay, N., Birkbeck, N., Zhang, J., Zhou, S.K.: Rapid Multi-organ Segmentation Using Context Integration and Discriminative Models. In: Gee, J.C., Joshi, S., Pohl, K.M., Wells, W.M., Zöllei, L. (eds.) IPMI 2013. LNCS, vol. 7917, pp. 450– 462. Springer, Heidelberg (2013)
13. Lu, C., Zheng, Y., Birkbeck, N., Zhang, J., Kohlberger, T., Tietjen, C., Boettger, T., Duncan, J.S., Zhou, S.K.: Precise Segmentation of Multiple Organs in CT Volumes Using Learning-Based Approach and Information Theory. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) MICCAI 2012, Part II. LNCS, vol. 7511, pp. 462–469. Springer, Heidelberg (2012)