Self-assessed performance improves statistical fusion of image labels

Medical Physics

Volumn 41, Issue 3, 2014, Pages

  Bryan, Frederick W ^a   Xu, Zhoubing ^a   Asman, Andrew J ^a   Allen, Wade M ^a   Reich, Daniel S ^b   Landman, Bennett A ^a,c  

^a VANDERBILT UNIVERSITY   (United States)

^b NATIONAL INSTITUTE OF NEUROLOGICAL DISORDERS AND STROKE   (United States)

^c VANDERBILT UNIVERSITY   (United States)

Author keywords

confidence; rater model; self assessment; STAPLE; WebMill

Indexed keywords

AUTOMATION; IMAGE SEGMENTATION; MAGNETIC RESONANCE IMAGING; STATISTICS;

CONFIDENCE; INTER-INDIVIDUAL DIFFERENCES; SELF ASSESSMENT; SOURCES OF INFORMATIONS; STAPLE; STATISTICAL ASSESSMENT; STATISTICAL LABEL FUSIONS; WEBMILL;

IMAGE ENHANCEMENT;

ACCURACY; ALGORITHM; ARTICLE; EXPERIMENTAL STUDY; GOLD STANDARD; HUMAN; NUCLEAR MAGNETIC RESONANCE IMAGING; OBSERVATION; PERFORMANCE; POPULATION; SELF EVALUATION; SPINAL CORD; TRAINING; UNIVERSITY; AUTOMATED PATTERN RECOGNITION; AUTOMATION; COMPUTER ASSISTED DIAGNOSIS; COMPUTER PROGRAM; IMAGE PROCESSING; METHODOLOGY; OBSERVER VARIATION; PATHOLOGY; REPRODUCIBILITY; STATISTICAL MODEL;

ALGORITHMS; AUTOMATION; HUMANS; IMAGE INTERPRETATION, COMPUTER-ASSISTED; IMAGE PROCESSING, COMPUTER-ASSISTED; MAGNETIC RESONANCE IMAGING; MODELS, STATISTICAL; OBSERVER VARIATION; PATTERN RECOGNITION, AUTOMATED; REPRODUCIBILITY OF RESULTS; SOFTWARE; SPINAL CORD;

EID: 84894234576     PISSN: 00942405     EISSN: None     Source Type: Journal    
DOI: 10.1118/1.4864236     Document Type: Article

References (36)

1. M. Kaus et al., "Segmentation of meningiomas and low grade gliomas in MRI," in Medical Image Computing and Computer-Assisted Intervention-MICCAI'99 (Springer Berlin Heidelberg, 1999).
2. T. Kapur et al., "Segmentation of brain tissue from magnetic resonance images," Med. Image Anal. 1 (2), 109-127 (1996). 10.1016/S1361-8415(96)80008-9 (Pubitemid 126421959)
3. M. Prastawa et al., "Automatic brain tumor segmentation by subject specific modification of atlas priors," Acad. Radiol. 10 (12), 1341-1348 (2003). 10.1016/S1076-6332(03)00506-3 (Pubitemid 37531873)
4. S. Amari, A. Cichocki, and H. H. Yang, A New Learning Algorithm for Blind Signal Separation, Advances in Neural Information Processing Systems Vol. 8 (MIT Press, Cambridge, MA, 1996), pp. 757-763.
5. D. L. Collins et al., "Automatic 3-D model-based neuroanatomical segmentation," Human Brain Mapp. 3 (3), 190-208 (1995). 10.1002/hbm.460030304 (Pubitemid 3036541)
6. J. L. Tanabe et al., "Tissue segmentation of the brain in Alzheimer disease," Am. J. Neuroradiol. 18 (1), 115-123 (1997). (Pubitemid 27036639)
7. J. J. Corso et al., "Efficient multilevel brain tumor segmentation with integrated Bayesian model classification," IEEE Trans. Med. Imaging 27 (5), 629-640 (2008). 10.1109/TMI.2007.912817 (Pubitemid 351634371)
8. O. Tsang et al., "Comparison of tissue segmentation algorithms in neuroimage analysis software tools," in Engineering in Medicine and Biology Society (EMBS), 30th Annual International Conference of the IEEE, Vancouver, BC (IEEE, 2008). 10.1109/IEMBS.2008.4650068
9. T. C. Huang et al., "Semi-automated CT segmentation using optic flow and Fourier interpolation techniques," Comput. Methods Programs Biomed. 84 (2-3), 124-134 (2006). 10.1016/j.cmpb.2006.09.003 (Pubitemid 46053602)
10. A. X. Falcão et al., "User-steered image segmentation paradigms: Live wire and live lane," Graph. Models Image Process. 60 (4), 233-260 (1998). 10.1006/gmip.1998.0475
11. B. A. Landman et al., "Foibles, follies, and fusion: Web-based collaboration for medical image labeling," Neuroimage 59 (1), 530-539 (2012). 10.1016/j.neuroimage.2011.07.085
12. M. J. Cardoso et al., "LoAd: A locally adaptive cortical segmentation algorithm," NeuroImage 56 (3), 1386-1397 (2011). 10.1016/j.neuroimage.2011.02.013
13. M. Kaus et al., "Adaptive template moderated brain tumor segmentation in MRI," Bildverarbeitung fuer die Medizin 102-106 (1999).
14. M. Prastawa et al., "A brain tumor segmentation framework based on outlier detection," Med. Image Anal. 8 (3), 275-283 (2004). 10.1016/j.media.2004.06.007 (Pubitemid 39185911)
15. G. Cohen et al., "Segmentation techniques for the classification of brain tissue using magnetic resonance imaging," Psychiatry Res.: Neuroimaging 45 (1), 33-51 (1992). 10.1016/0925-4927(92)90012-S
16. D. L. Pham, and J. L. Prince, "Adaptive fuzzy segmentation of magnetic resonance images," IEEE Trans. Med. Imaging 18 (9), 737-752 (1999). 10.1109/42.802752 (Pubitemid 30519193)
17. R. M. Gardner, D. L. Brown, and R. Boice, "Using Amazon's Mechanical Turk website to measure accuracy of body size estimation and body dissatisfaction," Body Image 9 (4), 532-534 (2012). 10.1016/j.bodyim.2012. 06.006
18. W. Mason and S. Suri, "Conducting behavioral research on Amazon's Mechanical Turk," Behav. Res. Methods 44 (1), 1-23 (2012). 10.3758/s13428-011-0124-6
19. D. G. Rand, "The promise of Mechanical Turk: How online labor markets can help theorists run behavioral experiments," J. Theor. Biol. 299, 172-9 (2012). 10.1016/j.jtbi.2011.03.004
20. B. A. Landman et al., "Robust statistical fusion of image labels," IEEE Trans. Med. Imaging 31 (2), 512-522 (2011). 10.1109/TMI.2011.2172215
21. S. K. Warfield, K. H. Zou, and W. M. Wells, "Simultaneous truth and performance level estimation (STAPLE): An algorithm for the validation of image segmentation," IEEE Trans. Med. Imaging 23 (7), 903-921 (2004). 10.1109/TMI.2004.828354
22. J. Talairach, Atlas d'anatomie stéréotaxique du télencéphale: études anatomo-radiologiques [Atlas of Stereo-taxic Anatomy of the Telencephalon] (Masson et Cie (Masson & Co.), 1967).
23. A. Asman and B. Landman, "Robust statistical label fusion through consensus level, labeler accuracy and truth estimation (COLLATE)," IEEE Trans. Med. Imaging 30 (10), 1779-1794 (2011). 10.1109/TMI.2011.2147795
24. A. J. Asman and B. A. Landman, "Formulating spatially varying performance in the statistical fusion framework," IEEE Trans. Med. Imaging 31 (6), 1326-1336 (2012). 10.1109/TMI.2012.2190992
25. A. J. Asman et al., "Foibles, follies, and fusion: Assessment of statistical label fusion techniques for web-based collaborations using minimal training," Proc. SPIE 7962, 79623G (2011). 10.1117/12.877471
26. O. Commowick, A. Akhondi-Asl, and S. K. Warfield, "Estimating a reference standard segmentation with spatially varying performance parameters: Local MAP STAPLE," IEEE Trans. Med. Imaging 31 (8), 1593-1606 (2012). 10.1109/TMI.2012.2197406
27. M. R. Sabuncu et al., "A generative model for image segmentation based on label fusion," IEEE Trans. Med. Imaging 29 (10), 1714-1729 (2010). 10.1109/TMI.2010.2050897
28. R. Wolz et al., "Multi-organ abdominal CT segmentation using hierarchically weighted subject-specific atlases," in Medical Image Computing and Computer-Assisted Intervention-MICCAI 2012, Nice, France (Springer, Berlin, Heidelberg, 2012), pp. 10-17.
29. A. P. Dempster, N. M. Laird, and D. B. Rubin, "Maximum likelihood from incomplete data via the EM algorithm," J. R. Stat. Soc., Ser. B 39, 1-38 (1977).
30. T. Rohlfing, D. B. Russakoff, and C. R. Maurer, "Performance-based classifier combination in atlas-based image segmentation using expectation-maximization parameter estimation," IEEE Trans. Med. Imaging 23 (8), 983-994 (2004). 10.1109/TMI.2004.830803
31. A. J. Asman et al., "Robust GM/WM segmentation of the spinal cord with iterative non-local statistical fusion," in Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2013 (Springer, Nagoya, Japan, 2013).
32. B. M. Dawant et al., "Automatic 3-D segmentation of internal structures of the head in MR images using a combination of similarity and free-form transformations. I. Methodology and validation on normal subjects," IEEE Trans. Med. Imaging 18 (10), 909-916 (1999). 10.1109/42.811271 (Pubitemid 32211106)
33. J. M. Lee et al., "Evaluation of automated and semi-automated skull-stripping algorithms using similarity index and segmentation error," Comput. Biol. Med. 33 (6), 495-507 (2003). 10.1016/S0010-4825(03)00022-2 (Pubitemid 38352768)
34. A. Evans et al., "MRI-PET correlation in three dimensions using a volume-of-interest (VOI) atlas," J. Cereb. Blood Flow Metab. 11, A69-A78 (1991). 10.1038/jcbfm.1991.40
35. M. I. Jordan and R. A. Jacobs, "Hierarchical mixtures of experts and the EM algorithm," Neural Comput. 6 (2), 181-214 (1994). 10.1162/neco.1994.6.2.181
36. 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. 25 (3), 343-353 (2003). 10.1109/TPAMI.2003.1182097