Automatic liver segmentation using statistical prior models and free-form deformation

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

Volumn 8848, Issue , 2014, Pages 181-188

  Li, Xuhui ^a,b   Huang, Cheng ^a   Jia, Fucang ^a   Li, Zongmin ^b   Fang, Chihua ^c   Fan, Yingfang ^c  

^a SHENZHEN INSTITUTES OF ADVANCED TECHNOLOGY   (China)

^b CHINA UNIVERSITY OF PETROLEUM   (China)

^c SOUTHERN MEDICAL UNIVERSITY   (China)

Author keywords

Free form deformation; Liver segmentation; Model localization; Model reconstruction; Prior model

Indexed keywords

ADAPTIVE BOOSTING; BIG DATA; COMPUTER VISION; COMPUTERIZED TOMOGRAPHY; DEFORMATION; MEDICAL IMAGING;

ADA BOOST CLASSIFIERS; FREE-FORM DEFORMATION; LIVER SEGMENTATION; MODEL RECONSTRUCTION; PRIOR KNOWLEDGE; PRIOR MODELING; SEGMENTATION METHODS; SHAPE AND APPEARANCE MODEL;

IMAGE SEGMENTATION;

EID: 84917730331     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-319-13972-2_17     Document Type: Conference Paper

References (11)

1. Gauriau, R., Cuingnet, R., Prevost, R., Mory, B., Ardon, R., Lesage, D., Bloch, I.: A generic, robust and fully-automatic workflow for 3D CT liver segmentation. In: Yoshida, H., Warfield, S., Vannier, M.W. (eds.) Abdominal Imaging 2013. LNCS, vol. 8198, pp. 241–250. Springer, Heidelberg (2013)
2. Kainmüller, D., Lange, T., Lamecker, H.: Shape constrained automatic segmentation of the liver based on a heuristic intensity model. In: Hiemann, T., Styner, M., van Ginneken, B. (eds.) 3D Segmentation in the Clinic: A Grand Challenge, pp. 109-116 (2007)
3. Cootes, T.F., Taylor, C.J., Cooper, D.H., Graham, J.: Active shape models-their training and application. Comput. Vis. Image Understand. 61(1), 38–59 (1995)
4. Huang, C., Jia, F., Fang, C., Fan, Y., Hu, Q.: Automatic liver detection and segmentation from 3D CT images: a hybrid method using statistical pose model and probabilistic atlas. Int. J. Comput. Assist. Radiol. Surg. 8(S1), 237–238 (2013)
5. Thevenaz, P., Unser, M.: Optimization of mutual information for multiresolution image registration. IEEE Trans. Image Process. 9(12), 2083–2099 (2000)
6. Luthi, M., Blanc, R., Albrecht, T., Gass, T., Goksel, O., Buchler, P., Kistler, M., Bouslei-man, H., Reyes, M., Cattin, P., Vetter, T.: Statismo - A framework for PCA based statistical models. Insight J. 1, 1–18 (2012)
7. Ben Younes, L., Nakajima, Y., Saito, T.: Fully automatic segmentation of the Femur from 3D-CT images using primitive shape recognition and statistical shape models. Int. J. Comput. Assist. Radiol. Surg. 9(2), 189–196 (2014)
8. Wang, H., Das, S.R., Suh, J.W., Altinay, M., Pluta, J., Craige, C., Avants, B., Yushkevich, P.A.: A learning-based wrapper method to correct systematic errors in automatic image segmentation: Consistently improved performance in hippocampus, cortex and brain segmentation. Neuroimage 55(3), 968–985 (2011)
9. Nyúl, L.G., Udupa, J.K., Zhang, X.: New variants of a method of MRI scale standardization. IEEE Trans. Med. Imag. 19(2), 143–150 (2000)
10. Berendsen, F.F., van der Heide, U.A., Langerak, T.R., Kotte, A.N.T.J., Pluim, J.P.W.: Freeform image registration regularized by a statistical shape model: application to organ segmentation in cervical MR. Comput. Vis. Image Understand. 117(9), 1119–1127 (2013)
11. Montagnat, J., Delingette, H.: Volumetric medical images segmentation using shape constrained deformable models. In: Troccaz, J., Mösges, R., Grimson, W.L. (eds.) CVRMed-MRCAS 1997. LNCS, vol. 1205. Springer, Heidelberg (1997)