Fully automatic multi-organ segmentation based on multi-boost learning and statistical shape model search

CEUR Workshop Proceedings

Volumn 1390, Issue January, 2015, Pages 18-21

  He, Baochun ^a   Huang, Cheng ^a   Jia, Fucang ^a  

^a SHENZHEN INSTITUTES OF ADVANCED TECHNOLOGY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

BIOLOGICAL ORGANS; COMPUTERIZED TOMOGRAPHY; LOCATION; MEDICAL IMAGING;

GENERALIZATION ABILITY; GRADIENT FEATURE; MULTI-ORGAN SEGMENTATIONS; RELATIVE LOCATION; SHAPE AND APPEARANCE MODEL; SPLEEN SEGMENTATIONS; STATISTICAL SHAPE MODEL; STATISTICAL SHAPES;

CLASSIFICATION (OF INFORMATION);

EID: 84937719487     PISSN: 16130073     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (6)

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2. M. A. Selver Segmentation of abdominal organs from CT using a multi-level, hierarchical neural network strategy. Computer Methods and Programs in Biomedicine, 113(3):830-852, 2014.
3. C. Wang, O. Smedby. Automatic multi-organ segmentation in nonenhanced CT datasets using hierarchical shape priors. The 22nd International Conference on Pattern Recognition (ICPR), pp. 3327-3332, 2014.
4. X. Li, C. Huang, F. Jia, Z. Li, C. Fang, Y. Fan. Automatic liver segmentation using statistical prior models and free-form deformation. In: Menze, B., et al. (eds.) MCV 2014. LNCS, vol. 8848, pp. 181-188. Springer, Heidelberg (2014).
5. D. Benbouzid, R. Busa-Fekete, N. Casagrande, F.-D. Collin, B. Kgl. MULTIBOOST: a multi-purpose boosting package. The Journal of Machine Learning Research, 13, pp. 549-553, 3/1/2012
6. T. Heimann, H. P. Meinzer, I. Wolf. A statistical deformable model for the segmentation of liver CT volumes. MICCAI Workshop: 3D Segmentation in the clinic: A grand challenge, 161-166, 2007.