Three validation metrics for automated probabilistic image segmentation of brain tumours

Statistics in Medicine

Volumn 23, Issue 8, 2004, Pages 1259-1282

  Zou, Kelly H ^a,b   Wells III, William M ^a,c   Kikinis, Ron ^a   Warfield, Simon K ^a  

^a BRIGHAM AND WOMEN S HOSPITAL   (United States)

^b HARVARD MEDICAL SCHOOL   (United States)

^c MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

Author keywords

Dice similarity coefficient (DSC); Expectation maximization (EM) algorithm; Mutual information; Receiver operating characteristic (ROC) curve; Sensitivity; Specificity

Indexed keywords

ASTROCYTOMA; AUTOMATION; BRAIN SURGERY; BRAIN TUMOR; CLINICAL ARTICLE; CONTROLLED STUDY; CORRELATION ANALYSIS; DATA ANALYSIS; DIAGNOSTIC ACCURACY; GLIOMA; HUMAN; IMAGE ANALYSIS; IMAGE PROCESSING; IMAGE QUALITY; MATHEMATICAL COMPUTING; MEDICAL DECISION MAKING; MEDICAL EXPERT; MENINGIOMA; MONTE CARLO METHOD; NUCLEAR MAGNETIC RESONANCE IMAGING; PROBABILITY; RECEIVER OPERATING CHARACTERISTIC; REVIEW; SENSITIVITY AND SPECIFICITY; STANDARD; STATISTICAL MODEL; TREATMENT PLANNING; TUMOR CLASSIFICATION; VALIDATION PROCESS;

ALGORITHMS; BRAIN NEOPLASMS; DATA INTERPRETATION, STATISTICAL; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; MAGNETIC RESONANCE IMAGING; MARKOV CHAINS; MODELS, STATISTICAL; MONTE CARLO METHOD; ROC CURVE; SENSITIVITY AND SPECIFICITY;

EID: 1942520366     PISSN: 02776715     EISSN: None     Source Type: Journal    
DOI: 10.1002/sim.1723     Document Type: Review

References (57)

1. Pham DL, Xu CY, Prince JL. Current methods in medical image segmentation. Annual Review of Biomedical Engineering 2000; 2:315-325.
2. Cline HE, Lorensen, Kikinis R, Jolesz F. Three-Dimensional segmentation of MR images of the head using probability and connectivity. Journal of Computer Assisted Tomography 1990; 14:1037-1045.
3. Rogowska J. Overview and fundamentals of medical image segmentation. Handbook of Medical Imaging. Academic Press: San Diego, 2000; 69-85.
4. Goldszal AF, Pham DL. Volumetric segmentation. Handbook of Medical Imaging. Academic Press: San Diego, 2000; 185-211.
5. Warfield SK, Kaus MR, Jolesz FA, Kikinis R. Adaptive template moderated spatially varying statistical classification. Proceedings of the 1st International Conference on Medical Image Computing and Computer-Assisted Intervention. Boston, MA, 1998; 431-438.
6. Warfield SK, Westin CF, Guttmann CRG, Albert M, Jolesz FA, Kikinis R. Fractional segmentation of white matter. Proceedings of 2nd International Conference on Medical Imaging Computing and Computer Assisted Interventions, Springer: Cambridge, U.K., 1999.
7. Kaus M, Warfield SK, Nabavi A, Black PM, Jolesz FA, Kikinis, R. Automated segmentation of MRI of brain tumours. Radiology 2001; 218:586-591.
8. De Bonet JS, Viola P, Fisher JW III. Flexible histograms: a multiresolution target discrimination model. In Zelnio EG (ed.), Proceedings of SPIE, vol. 3370, 1998.
9. De Bonet JS, Viola P. Texture recognition using a non-parametric multi-scale statistical model. Proceedings of Computer Vision and Pattern Recognitions Conference 1998.
10. Santago P, Gage HD. Quantification of MR brain images by mixture density and partial volume modeling. IEEE Transaction on Medical Imaging 1993; 12:566-574.
11. Fleiss JL. Statistical Methods for Rates and Proportions. Wiley: New York, 1981; 212-236.
12. Zijdenbos AP, Dawant BM, Margolin RA, Palmer AC. Morphometric analysis of white matter lesions in MR images: method and validation. IEEE Transactions on Medical Imaging 1994; 13:716-724.
13. Jaccard P. The distribution of flora in the alpine zone. New Phytologist 1912; 11:37-50.
14. Dice LR. Measures of the amount of ecologic association between species. Ecology 1945; 26:297-302.
15. Ralescu AL, Ralescu DA. Probability and fuzziness. Information Science 1984; 34:85-92.
16. Shapiro DE. The interpretation of diagnostic tests. Statistical Methods in Medical Research 1999; 8:113-134.
17. Zou KH, Zhou XH. Receiver operating characteristic (ROC) analysis. Statistics in Epidemiology Report (Summer Version), Statistics in Epidemiology Section, American Statistical Association. URL: http://www.epm.ornl.gov/asasie/newsletter/SIEv2001n1.pdf, 2001.
18. Zhou XH, McClish DK, Obuchowski NA. Statistical Methods in Diagnostic Medicine. Wiley: New York, 2002.
19. Zou KH, Hall WJ, Shapiro DE. Smooth nonparametric receiver operating characteristic curves for continuous diagnostic tests. Statistics in Medicine 1997; 16:2143-2156.
20. Zou KH, Hall WJ. Two transformation models for estimating an ROC curve derived from continuous data. Journal of Applied Statistics 2000; 27:621-631.
21. Zou KH, Hall WJ. Semiparametric and parametric transformation models for comparing diagnostic markers with paired design. Journal of Applied Statistics 2002; 29:803-816.
22. Pepe MS. A regression modeling framework for ROC curves in medical diagnostic testing. Biometrika 1997; 84:595-608.
23. Pepe MS. Three approaches to regression analysis of receiver operating characteristic curves for continuous test results. Biometrics 1998; 54:124-135.
24. Pepe MS. An interpretation for the ROC curve and inference using GLM procedures. Biometries 2000; 56: 352-359.
25. O'Malley AJ, Zou KH, Fielding JR, Tempany CMC. Bayesian regression methodology for receiver operating characteristic curve with two radiologic applications: prostate biopsy and spiral CT of ureteral stones. Academic Radiology 2001; 8:713-725.
26. Haralick R, Shanmugan K, Dinstein I. Texture for image classification. IEEE Transactions on Systems, Mechanics, and Cybernetics 1973; 3:610-621.
27. Wieand S, Gail MH, James BR, James BR. A family of nonparametric statistics for comparing diagnostic markers with paired or unpaired data. Biometrika 1989; 76:585-592.
28. Hettmansperger TP. Statistical Inference Based on Ranks 1991; 132-177.
29. Brown D, Rothery P. Models in Biology: Mathematics, Statistics and Computing. Wiley: New York, 1993.
30. Campbell G. Advances in statistical methodology for the evaluation of diagnostic and laboratory tests. Statistics in Medicine 1994; 13:499-508.
31. Chan I, Wells WM, Mulkern RV, Haker S, Zhang J, Zou KH, Maier SE, Tempany CMC. Detection of prostate cancer by integration of line-scan diffusion, T2-mapping and T2-weighted MR imaging; a multi-channel statistical classifier. Medical physics 2003; 2390-2398.
32. Halpern EJ, Albert M, Krieger AM, Metz CE, Maidment AD. Comparison of receiver operating characteristic curves on the basis of optimal operating point. Academic Radiology 1996; 3:245-253.
33. Sainfort F. Evaluation of medical technologies: a generalized ROC analysis. Medical Decision Making 1991; 11:208-220.
34. Englang WL. An exponential model used for optimal threshold selection on ROC curves. Medical Decision Making 1988; 8:120-131.
35. Shaffer H. Constructing a cut-off point for a quantitative diagnostic test. Statistics in Medicine 1989; 8: 1381-1391.
36. Phelps CE, Mushlin AI. Focusing technology assessment using medical decision theory. Medical Decision Making 1988; 8:278-289.
37. Somoza E, Mossman D. Comparing and optimizing diagnostic tests: an information-theory approach. Medical Decision Making 1992; 12:179-188.
38. Swets JA. Signal Detection Theory and ROC Analysis in Psychology and Diagnostics. Lawrence Erlbaum Associates, Inc.: Mahwah, NJ, 1996; 158-160.
39. Starr SJ, Metz CE, Lusted LB, Goodenough DJ. Visual detection and localization of radiographic images. Radiology 1975; 116:533-538.
40. Swensson RG. Unified measurement of observer performance in detecting and localizing target objects on images. Medical physics 1996; 23:1709-1725.
41. Worsley KJ, Marrett S, Neelin P, Vandal AC, Friston JJ, AC. A unified statistical approach for determining significant signals in images of cerebral activation. Human Brain Mapping 1996; 4:58-73.
42. Friston KJ, Ashburner J, Poline JB, Frith CD, Heather JD, Frackowiak RSJ. Spatial registration and normalisation of images. Human Brain Mapping 1995; 2:165-189.
43. Rajapakse JC. Piyaratna J. Bayesian approach to segmentation of statistical parametric maps. IEEE Transactions on Biomedical Engineering 2001; 48:1186-1194.
44. Zhang Y, Brady M, Smith S. Segmentation of brain MR images through a hidden Markov random field model and the expectation maximization algorithm. IEEE Transactions on Medical Imaging 2001; 20: 45-57.
45. Zhang J. The mean field theory in EM procedure for Markov random fields. IEEE Transactions on Signal Processing 1992; 40:2570-2583.
46. Qu Y and Hadgu A. A model for evaluating sensitivity and specificity for correlated diagnostics tests in efficacy studies with an imperfect reference test. Journal of American Statistical Association 1998; 93:920-928.
47. Joseph L, Gyorkos TW, Coupal L. Bayesian estimation of disease prevalence and the parameters of diagnostic tests in the absence of a gold standard. American Journal of Epidemiology 1995; 141:263-272.
48. Warfield SK, Zou KH, Kaus MR, Wells WM III. Simultaneous validation of image segmentation and assessment of expert quality. Proceedings of ISBI Conference. IEEE: New York, vol. 1494, 2002; 1-4.
49. Warfield SK, Zou KH, Wells WM III. Validation of image segmentation and expert quality with an expectation-maximization algorithm. Proceedings of 5th International Conference on Medical Imaging Computing and Computer Assisted Interventions, Tokyo, Japan. Springer: New York, vol. 2488, 2002; 298-306.
50. Dempster AP, Laird NM, Rubin DB. Validation of image segmentation and expert quality with an expectation-maximization algorithm. Journal of Royal Statistical Society (Series B) 1977; 39:34-37.
51. McLachlan GJ, Krishnan T. The EM Algorithm and Extensions. Wiley: New York, 1997.
52. Gelman A, Carlin JB, Stern HS, Rubin DB. Bayesian Data Analysis. Chapman & Hall: London, 1995; 28-32.
53. Hahn GJ, Shapiro SS. Statistical Models in Engineering. Wiley: New York. 1994; 95.
54. Cover TMM, Thomas JA. Elements of Information Theory. Wiley: New York, 1991.
55. Zou KH, Wells M III., Kaus MR, Kikinis R, Jolesz FA, Warfield SK. Statistical validation of automated probabilistic segmentation against composite latent expert ground truth in MR imaging of brain tumours. Proceedings of 5th International Conference on Medical Imaging Computing and Computer Assisted Interventions, Tokyo, Japan. Springer: New York, vol. 2488, 2002; 315-322.
56. Greig DM, Porteous BT, Seheult AH. Exact maximum a posterior estimation for binary images. Journal of Royal Statistical Society (Series B), 1989; 51:271-279.
57. Kapur T, Grimson L, Wells WM, Kikinis R. Segmentation of brain tissue from magnetic resonance images. Medical Image Analysis 1996; 1:109-127.