Robust statistical label fusion through consensus level, labeler accuracy, and truth estimation (COLLATE)

IEEE Transactions on Medical Imaging

Volumn 30, Issue 10, 2011, Pages 1779-1794

  Asman, Andrew J ^a   Landman, Bennett A ^a  

^a VANDERBILT UNIVERSITY   (United States)

Author keywords

Consensus level, labeler accuracy and truth estimation (COLLATE); data fusion; delineation; labeling; parcellation; simultaneous truth and performance level estimation (STAPLE); statistical analysis

Indexed keywords

CONSENSUS LEVEL, LABELER ACCURACY AND TRUTH ESTIMATION (COLLATE); DATA SETS; DELINEATION; FUNCTIONAL CORRELATION; FUSION ALGORITHMS; FUSION METHODS; GOLD STANDARDS; GROUND TRUTH; HIGH CONTRAST; HUMAN OBSERVERS; INTEROBSERVER VARIABILITY; LARGE REGIONS; LOW CONTRAST; MEDICAL IMAGES; NOVEL STRUCTURES; PARCELLATION; PERFORMANCE LEVEL; SINGLE OBSERVER; STATISTICAL APPROACH; STATISTICAL POWER; VARYING MODELS;

ALGORITHMS; DATA FUSION; EDGE DETECTION; IMAGE SEGMENTATION; LABELING; MEDICAL IMAGING; STATISTICAL METHODS; STATISTICS;

ESTIMATION;

ALGORITHM; ARTICLE; COMPUTER SIMULATION; DIAGNOSTIC IMAGING; FACTUAL DATABASE; HUMAN; IMAGE PROCESSING; METHODOLOGY; MONTE CARLO METHOD; STATISTICAL MODEL;

ALGORITHMS; COMPUTER SIMULATION; DATABASES, FACTUAL; DIAGNOSTIC IMAGING; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; MODELS, STATISTICAL; MONTE CARLO METHOD;

EID: 80053499342     PISSN: 02780062     EISSN: None     Source Type: Journal    
DOI: 10.1109/TMI.2011.2147795     Document Type: Article

References (39)

1. S. Warfield et al., "Automatic identification of gray matter structures from MRI to improve the segmentation of white matter lesions," J. Image Guided Surg., vol. 1, pp. 326-338, 1995.
2. R. Kikinis et al., "Routine quantitative analysis of brain and cerebrospinal fluid spaces with MR imaging," J. Magn. Reson. Imag., vol. 2, pp. 619-629, Nov.-Dec. 1992.
3. T. K. Ho et al., "Decision combination in multiple classifier systems," IEEE Trans. Pattern Anal. Mach. Intell., vol. 16, no. 1, pp. 66-75, Jan. 1994.
4. J. Kittler et al., "On combining classifiers," IEEE Trans. Pattern Anal. Mach. Intell., vol. 20, no. 3, pp. 226-239, Mar. 1998. (Pubitemid 128741312)
5. D. Windridge and J. Kittler, "A morphologically optimal strategy for classifier combination: Multiple expert fusion as a tomographic process," IEEE Trans. Pattern Anal. Mach. Intell., vol. 25, no. 3, pp. 343-353, Mar. 2003.
6. R. Jacobs et al., "Adaptive mixtures of local experts," Neural Computat., vol. 3, pp. 79-87, 1991.
7. M. Jordan and R. Jacobs, "Hierarchical mixtures of experts and the EM algorithm," Neural Computat., vol. 6, pp. 181-214, 1994.
8. A. Alonzo and H. Pepe, "Using a combination of reference tests to assess the accuracy of a diagnostic test," Stat.Med., vol. 20, pp. 656-658, Feb. 2001.
9. S. Beiden et al., "The problem of ROC analysis without truth: The EM algorithm and the information matrix," in SPIE Med. Imag., 2000, p. 126.
10. M. Kearns and L. G. Valiant, Learning boolean formulae or finite automata is as hard as factoring Harvard Univ., Cambridge, MA, Learning boolean formulae or finite automata is as hard as factoring, 1988.
11. R. E. Shapire, "The strength of weak learnability," Mach. Learn., vol. 5, pp. 197-227, 1990.
12. Y. Freund and R. E. Schapire, "A decision-theoretic generalization of on-line learning and an application to boosting," J. Comput. Syst. Sci., vol. 55, 1997.
13. S. K. Warfield et al., "Simultaneous validation of image segmentation and assessment of expert quality," presented at the Int. Symp. Biomed. Imag., Washington, DC, 2002.
14. S. K. Warfield et al., "Simultaneous truth and performance level estimation (STAPLE): An algorithm for the validation of image segmentation," IEEE Trans. Med. Imag., vol. 23, no. 7, pp. 903-921, Jul. 2004.
15. T. Rohlfing et al., "Expectation maximization strategies for multi-atlas multi-label segmentation," Inf. Process. Med. Imag., vol. 18, pp. 210-221, Jul. 2003.
16. J. Udupa et al., "A framework for evaluating image segmentation algorithms," Comp. Med. Imag. Graphics, vol. 30, pp. 75-87, 2006.
17. R. A. Heckemann et al., "Automatic anatomical brain MRI segmentation combining label propagation and decision fusion," Neuroimage, vol. 33, pp. 115-126, 1006.
18. T. R. Langerak et al., "Label fusion using performance estimation with iterative label selection," in IEEE Int. Symp. Biomed. Imag.: From Nano to Micro, Boston, MA, 2009, pp. 1186-1189.
19. J. M. Lotjonen et al., "Fast and robustmulti-atlas segmentation of brain magnetic resonance images," Neuroimage, vol. 49, pp. 2352-2365, Feb. 2010.
20. S. K. Warfield et al., "Validation of image segmentation by estimating rater bias and variance," Philos. Trans. R. Soc., vol. 366, pp. 2361-2375, 2008.
21. O. Commowick and S. K. War, "A continuous STAPLE for scalar, vector, and tensor images: An application to DTI analysis," IEEE Trans. Med. Imag, vol. 28, no. 6, pp. 838-846, Jun. 2009.
22. F. Xing et al., "Statistical fusion of continuous labels: Identification of cardiac landmarks," presented at the SPIE Med. Imag. Conf., Lake Buena Vista, FL, 2011.
23. B. Landman et al., "Simultaneous truth and performance level estimation with incomplete, over-complete, and ancillary data," presented at the SPIE Med. Imag. Conf., San Diego, CA, 2010.
24. J. A. Bogovic et al., "Statistical fusion of surface labels provided by multiple raters, over-complete, and ancillary data," presented at the SPIE Med. Imag. Conf., San Diego, CA, 2010.
25. O. Commowick and S. Warfield, "Incorporating priors on expert performance parameters for segmentation validation and label fusion: A maximum a posteriori STAPLE," Med. Image Comput. Computer-Assist. Intervent.-MICCAI 2010, pp. 25-32, 2010.
26. B. Landman et al., "Efficient anatomical labeling by statistical recombination of partially label datasets," presented at the Int. Soc. Magn. Reson. Med. Conf., Honolulu, HI, 2009.
27. A. J. Asman et al., "Foibles, follies, and fusion: Assessment of statistical label fusion techniques for web-based collaborations using minimal training," in SPIE Med. Imag. Conf., Lake Buena Vista, FL, 2011.
28. D. Collins, "3D model-based segmentation of individual brain structures from magnetic resonance imaging data," thesis, McGill Univ., Montreal, QC, Canada, 1994.
29. J. Rexilius et al., "A novel nonrigid registration algorithm and applications," in Med. Imag. Computing Computer-Assisted Intervent. (MICCAI), 2010, pp. 923-931.
30. G. McLachlan et al., The EM Algorithm and Extensions. NewYork: Wiley, 1997.
31. T. Moon, "The expectation-maximization algorithm," IEEE Signal Process. Mag., vol. 13, no. 6, pp. 47-60, Nov. 2002.
32. T. Rohlfing et al., "Performance-based classifier combination in atlasbased image segmentation using expectation-maximization parameter estimation," IEEE Trans. Med. Imag., vol. 23, no. 8, pp. 983-994, Aug. 2004.
33. J. A. Stein et al., "Characterizing and optimizing rater performance for internet-based collaborative labeling," in SPIE Med. Imag. Conf., Lake Buena Vista, FL, 2011.
34. L. Dice, "Measures of the amount of ecologic association between species," Ecology, vol. 26, pp. 297-302, 1945.
35. P. Jaccard, "The distribution of flora in the alpine zone," New Phytol., vol. 11, pp. 37-50, 1912.
36. A. P. Dempster et al., "Maximum likelihood from incomplete data via EM algorithm," J. R. Stat. Soc. Series B-Methodological, vol. 39, pp. 1-38, 1977.
37. J. Zhang, "Themean field theory in EMprocedures forMarkov random fields," IEEE Trans. Signal Process., vol. 40, no. 10, pp. 2570-2583, Oct. 1992. (Pubitemid 23555907)
38. T. Kapur et al., "Enhanced spatial priors for segmentation of magnetic resonance imagery," Medical Image Computing and Computer-Assisted Interventation-MICCAI'98, pp. 457-457, 1998.
39. J. Zhang and G. Hanauer, "The application of mean field theory to image motion estimation," IEEE Trans. Image Process., vol. 4, no. 1, pp. 19-33, Jan. 2002.