Confidence-guided sequential label fusion for multi-atlas based segmentation

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

Volumn 6893 LNCS, Issue PART 3, 2011, Pages 643-650

  Zhang, Daoqiang ^a,b   Wu, Guorong ^a   Jia, Hongjun ^a   Shen, Dinggang ^a  

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

^b NANJING UNIVERSITY OF AERONAUTICS AND ASTRONAUTICS   (China)

Author keywords

[No Author keywords available]

Indexed keywords

FINAL DECISION; FUSION ALGORITHMS; FUSION METHODS; FUSION PROCESS; HIGH CONFIDENCE; K-NEAREST-NEIGHBOR RULE; LABELING PROCEDURES; MAJORITY VOTING; WEIGHTED VOTING;

ALGORITHMS; MEDICAL COMPUTING;

IMAGE SEGMENTATION;

ALGORITHM; ARTICLE; AUTOMATED PATTERN RECOGNITION; BRAIN; BRAIN MAPPING; COMPUTER ASSISTED DIAGNOSIS; COMPUTER PROGRAM; FACTUAL DATABASE; FEMALE; HUMAN; IMAGE SUBTRACTION; MALE; METHODOLOGY; PATHOLOGY; REPRODUCIBILITY; STATISTICAL MODEL; THREE DIMENSIONAL IMAGING;

ALGORITHMS; BRAIN; BRAIN MAPPING; DATABASES, FACTUAL; FEMALE; HUMANS; IMAGE INTERPRETATION, COMPUTER-ASSISTED; IMAGING, THREE-DIMENSIONAL; MALE; MODELS, STATISTICAL; PATTERN RECOGNITION, AUTOMATED; REPRODUCIBILITY OF RESULTS; SOFTWARE; SUBTRACTION TECHNIQUE;

EID: 82255180835     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-23626-6_79     Document Type: Conference Paper

References (10)

1. Lotjonen, J., Wolz, R., Koikkalainen, J., Thurfjell, L., Waldemar, G., Soininen, H., Rueckert, D.: Fast and robust multi-atlas segmentation of brain magnetic resonance images. Neuroimage 49, 2352-2365 (2010)
2. Isgum, I., Staring, M., Rutten, A., Prokop, M., Viergever, M.A., van Ginneken, B.: Multi-atlas-based segmentation with local decision fusion-application to cardiac and aortic segmentation in CT scans. IEEE Trans. Med.l Imag. 28, 1000-1010 (2009)
3. Langerak, T.R., van der Heide, U.A., Kotte, A.N., Viergever, M.A., van Vulpen, M., Pluim, J.P.: Label fusion in atlas-based segmentation using a selective and iterative method for performance level estimation (SIMPLE). IEEE Trans. Med.l Imag. 29, 2000-2008 (2010)
4. Artaechevarria, X., Munoz-Barrutia, A., De Solorzano, C.O.: Combination strategies in multi-atlas image segmentation: Application to brain MR data. IEEE Trans. Med.l Imag. 28, 1266-1277 (2009)
5. Aljabar, P., Heckemann, R.A., Hammers, A., Hajnal, J.V., Rueckert, D.: Multi-atlas based segmentation of brain images: Atlas selection and its effect on accuracy. Neuroimage 46, 726-738 (2009)
6. Sabuncu, M.R., Yeo, B.T., van Leemput, K., Fischl, B., Golland, P.: A generative model for image segmentation based on label fusion. IEEE Trans. Med.l Imag. 29, 1714-1729 (2010)
7. Coupe, P., Manjon, J.V., Fonov, V., Pruessner, J.: Patch-based segmentation using expert priors: Application to hippocampus and ventricle segmentation. Neuroimage 54, 940-954 (2011)
8. Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification. John Wiley and Sons, Inc., Chichester (2000)
9. Christensen, G., Geng, X., Kuhl, J., Bruss, J., Grabowski, T., Pirwani, I., Vannier, M., Allen, J., Damasio, H.: Introduction to the non-rigid Image registration evaluation project (NIREP). In: Pluim, J.P.W., Likar, B., Gerritsen, F.A. (eds.) WBIR 2006. LNCS, vol. 4057, pp. 128-135. Springer, Heidelberg (2006) (Pubitemid 44113208)
10. Warfield, S.K., Zou, K.H., Wells, W.M.: Simultaneous truth and performance level estimation (STAPLE): An algorithm for the validation of image segmentation. IEEE Trans. Med.l Imag. 23, 903-921 (2004)