A collaborative resource to build consensus for automated left ventricular segmentation of cardiac MR images

Medical Image Analysis

Volumn 18, Issue 1, 2014, Pages 50-62

  Suinesiaputra, Avan ^a   Cowan, Brett R ^a   Al Agamy, Ahmed O ^c   Elattar, Mustafa A ^e   Ayache, Nicholas ^b   Fahmy, Ahmed S ^c,d   Khalifa, Ayman M ^e,i   Medrano Gracia, Pau ^a   Jolly, Marie Pierre ^h   Kadish, Alan H ^f   Lee, Daniel C ^f   Margeta, Ján ^b   Warfield, Simon K ^g   Young, Alistair A ^a  

^a UNIVERSITY OF AUCKLAND   (New Zealand)

^b INRIA   (France)

^c NILE UNIVERSITY   (Egypt)

^d CAIRO UNIVERSITY   (Egypt)

^e Diagnosoft Inc   (United States)

^f NORTHWESTERN UNIVERSITY   (United States)

^g HARVARD MEDICAL SCHOOL   (United States)

^h SIEMENS CORPORATE RESEARCH   (United States)

ⁱ Engineering Department   (Egypt)

Author keywords

Consensus images; LV myocardium; Segmentation challenge

Indexed keywords

CONSENSUS IMAGES; CORONARY ARTERY DISEASE; EXPECTATION MAXIMIZATION; LEFT VENTRICULAR MYOCARDIUMS; LV MYOCARDIUM; NEGATIVE PREDICTIVE VALUE; PERFORMANCE DISTRIBUTION; SEGMENTATION ALGORITHMS;

ALGORITHMS; AUTOMATION; EQUIPMENT TESTING; HEART; MAGNETIC RESONANCE IMAGING;

IMAGE SEGMENTATION;

ADULT; AGED; ALGORITHM; ARTICLE; AUTOMATION; CARDIOVASCULAR MAGNETIC RESONANCE; CORONARY ARTERY DISEASE; ENDOCARDIUM; FEMALE; HEART INFARCTION; HEART MUSCLE; HEART PAPILLARY MUSCLE; HUMAN; INTERNATIONAL NORMALIZED RATIO; MACHINE LEARNING; MAJOR CLINICAL STUDY; MALE; OPTIC FLOW; PREDICTIVE VALUE; PRIORITY JOURNAL; SENSITIVITY AND SPECIFICITY;

CONSENSUS IMAGES; LV MYOCARDIUM; SEGMENTATION CHALLENGE;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; CORONARY ARTERY DISEASE; FEMALE; HEART VENTRICLES; HUMANS; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; LIKELIHOOD FUNCTIONS; MAGNETIC RESONANCE IMAGING, CINE; MALE; MIDDLE AGED; PATTERN RECOGNITION, AUTOMATED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; SUBTRACTION TECHNIQUE; VENTRICULAR DYSFUNCTION, LEFT;

EID: 84884915206     PISSN: 13618415     EISSN: 13618423     Source Type: Journal    
DOI: 10.1016/j.media.2013.09.001     Document Type: Article

References (54)

1. Archip N., Jolesz F.A., Warfield S.K. A validation framework for brain tumor segmentation. Acad. Radiol. 2007, 14:1242-1251.
2. Bild D.E., Bluemke D.A., Burke G.L., Detrano R., Diez Roux A.V., Folsom A.R., Greenland P., Jacob D.R., Kronmal R., Liu K., Nelson J.C., O'Leary D., Saad M.F., Shea S., Szklo M., Tracy R.P. Multi-ethnic study of atherosclerosis: objectives and design. Am. J. Epidemiol. 2002, 156:871-881.
3. Chang H.H., Zhuang A.H., Valentino D.J., Chu W.C. Performance measure characterization for evaluating neuroimage segmentation algorithms. Neuroimage 2009, 47:122-135.
4. Cohen J. A coefficient of agreement for nominal scales. Educ. Psychol. Measur. 1960, 20:37-46.
5. Collins J., Mancilulli M., Hohlfeld R., Finch D., Sandri G., Shtatland E. A random number generator based on the logit transform of the logistic variable. Comput. Phys. 1992, 6:630-632.
6. Commowick O., Warfield S.K. A continuous STAPLE for scalar, vector, and tensor images: an application to DTI analysis. IEEE Trans. Med. Imag. 2009, 28:838-846.
7. Commowick O., Warfield S.K. Estimation of inferential uncertainty in assessing expert segmentation performance from STAPLE. IEEE Trans. Med. Imag. 2010, 29:771-780.
8. Criminisi A., Shotton J., Konukoglu E. Decision forests: a unified framework for classification, regression, density estimation, manifold learning and semi-supervised learning. Found. Trends Comput. Graph. Vis. 2012, 7:81-227.
9. Dempster A.P., Laird N.M., Rubin D.B. Maximum likelihood from incomplete data via EM algorithm. J. Roy. Stat. Soc. B: Met. 1977, 39:1-38.
10. Fahmy A., Al-Agamy A., Khalifa A. Myocardial segmentation using contour-constrained optical flow tracking. Statistical Atlases and Computational Models of the Heart. Imaging and Modelling Challenges 2012, 120-128. Springer.
11. Fonseca C.G., Backhaus M., Bluemke D.A., Britten R.D., Chung J.D., Cowan B.R., Dinov I.D., Finn J.P., Hunter P.J., Kadish A.H., Lee D.C., Lima J.A.C., Medrano-Gracia P., Shivkumar K., Suinesiaputra A., Tao W., Young A.A. The cardiac atlas project - an imaging database for computational modeling and statistical atlases of the heart. Bioinformatics 2011, 27:2288-2295.
12. Frangi A.F., Niessen W.J., Viergever M.A. Three-dimensional modeling for functional analysis of cardiac images: a review. IEEE Trans. Med. Imag. 2001, 20:2-25.
13. Fukunaga K. Introduction to Statistical Pattern Recognition 1990, Academic Press. second ed.
14. Geremia E., Clatz O., Menze B.H., Konukoglu E., Criminisi A., Ayache N. Spatial decision forests for MS lesion segmentation in multi-channel magnetic resonance images. Neuroimage 2011, 57:378-390.
15. van Ginneken B., Armato S.G., de Hoop B., van Amelsvoort-van de Vorst S., Duindam T., Niemeijer M., Murphy K., Schilham A., Retico A., Fantacci M.E., Camarlinghi N., Bagagli F., Gori I., Hara T., Fujita H., Gargano G., Bellotti R., Tangaro S., Bolaños L., De Carlo F., Cerello P., Cristian Cheran S., Lopez Torres E., Prokop M. Comparing and combining algorithms for computer-aided detection of pulmonary nodules in computed tomography scans: the ANODE09 study. Med. Image Anal. 2010, 14:707-722.
16. Guetter, C., Xue, H., Chefd'hotel, C., Guehring, J., 2011. Efficient symmetric and inverse-consistent deformable registration through interleaved optimization, in: 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 590-593.
17. Hameeteman K., Zuluaga M.A., Freiman M., Joskowicz L., Cuisenaire O., Valencia L.F., Gülsün M.A., Krissian K., Mille J., Wong W.C.K., Orkisz M., Tek H., Hoyos M.H., Benmansour F., Chung A.C.S., Rozie S., van Gils M., van den Borne L., Sosna J., Berman P., Cohen N., Douek P.C., Sánchez I., Aissat M., Schaap M., Metz C.T., Krestin G.P., van der Lugt A., Niessen W.J., van Walsum T. Evaluation framework for carotid bifurcation lumen segmentation and stenosis grading. Med. Image Anal. 2011, 15:477-488.
18. Hamou A.K., El-Sakka M.R. Optical flow active contours with primitive shape priors for echocardiography. EURASIP J. Adv. Signal Process 2010, 2010. 9:2-9:2.
19. Hoyte L., Ye W., Brubaker L., Fielding J.R., Lockhart M.E., Heilbrun M.E., Brown M.B., Warfield S.K. Segmentations of MRI images of the female pelvic floor: a study of inter- and intra-reader reliability. J. Magn. Reson. Imag. 2011, 33:684-691. Pelvic Floor Disorders Network.
20. Hwee J., Louie A.V., Gaede S., Bauman G., D'Souza D., Sexton T., Lock M., Ahmad B., Rodrigues G. Technology assessment of automated atlas based segmentation in prostate bed contouring. Radiat. Oncol. 2011, 6:110.
21. Ishida M., Kato S., Sakuma H. Cardiac MRI in ischemic heart disease. Circ. J. 2009, 73:1577-1588.
22. Jolly M.P. Automatic recovery of the left ventricular blood pool in cardiac cine MR images. Proceedings of the 11th International Conference on Medical Image Computing and Computer-Assisted Intervention - Part I 2008, 110-118. Springer-Verlag, Berlin, Heidelberg.
23. Jolly, M.P., Guetter, C., Guehring, J., 2010. Cardiac segmentation in MR cine data using inverse consistent deformable registration, in: 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 484-487.
24. Jolly M.P., Guetter C., Lu X., Xue H., Guehring J. Automatic segmentation of the myocardium in cine MR images using deformable registration. Statistical Atlases and Computational Models of the Heart. Imaging and Modelling Challenges 2012, 98-108. Springer.
25. Kadish A.H., Bello D., Finn J.P., Bonow R.O., Schaechter A., Subacius H., Albert C., Daubert J.P., Fonseca C.G., Goldberger J.J. Rationale and design for the defibrillators to reduce risk by magnetic resonance imaging evaluation (DETERMINE) trial. J. Cardiovasc. Electrophysiol. 2009, 20:982-987.
26. Karamitsos T.D., Myerson S.G. The role of cardiovascular magnetic resonance in the evaluation of valve disease. Prog. Cardiovasc. Dis. 2011, 54:276-286.
27. Kass M., Witkin A., Terzopoulos D. Snakes: active contour models. Int. J. Comput. Vis. 1988, 1:321-331.
28. Lam K.M., Yan H. Fast greedy algorithm for active contours. Electron. Lett. 1994, 30:21-22.
29. Lempitsky V.S., Verhoek M., Noble J.A., Blake A. Random forest classification for automatic delineation of myocardium in real-time 3D echocardiography. FIMH 2009, 447-456. Springer.
30. Li B., Liu Y., Occleshaw C.J., Cowan B.R., Young A.A. In-line automated tracking for ventricular function with magnetic resonance imaging. JACC Cardiovasc. Imag. 2010, 3:860-866.
31. Liu T., Li H., Wong K., Tarokh A., Guo L., Wong S.T.C. Brain tissue segmentation based on DTI data. Neuroimage 2007, 38:114-123.
32. Lo P., van Ginneken B., Reinhardt J., de Bruijne M. Extraction of airways from CT (EXACT'09). IEEE Trans. Med. Imag. 2012, 31:2093-2107.
33. Lu X., Georgescu B., Jolly M.P., Guehring J., Young A., Cowan B., Littmann A., Comaniciu D. Cardiac anchoring in MRI through context modeling. Proceedings of the 13th International Conference on Medical Image Computing and Computer-assisted Intervention: Part I 2010, 383-390. Springer-Verlag, Berlin, Heidelberg.
34. Margeta J., Geremia E., Criminisi A., Ayache N. Layered spatio-temporal forests for left ventricle segmentation from 4D cardiac MRI data. Statistical Atlases and Computational Models of the Heart. Imaging and Modelling Challenges 2012, 109-119. Springer.
35. Mikić I., Krucinski S., Thomas J.D. Segmentation and tracking in echocardiographic sequences: active contours guided by optical flow estimates. IEEE Trans. Med. Imag. 1998, 17:274-284.
36. Murphy K., van Ginneken B., Reinhardt J.M., et al. Evaluation of registration methods on thoracic CT: the EMPIRE10 challenge. IEEE Trans. Med. Imag. 2011, 30:1901-1920.
37. Muzzarelli S., Ordovas K., Higgins C.B. Cardiovascular MRI for the assessment of heart failure: focus on clinical management and prognosis. J. Magn. Reson. Imag. 2011, 33:275-286.
38. Nyúl L.G., Udupa J.K. On standardizing the MR image intensity scale. Magn. Reson. Med. 1999, 42:1072-1081.
39. Ollier W., Sprosen T., Peakman T. UK Biobank: from concept to reality. Pharmacogenomics 2005, 6:639-646.
40. Ourselin S., Roche A., Prima S., Ayache N. Block matching: a general framework to improve robustness of rigid registration of medical images. Medical Image Computing and Computer-Assisted Intervention - MICCAI 2000 2000, 557-566. Springer, Berlin/Heidelberg. S. Delp, A. DiGoia, B. Jaramaz (Eds.).
41. Paetsch I., Jahnke C., Ferrari V.A., Rademakers F.E., Pellikka P.A., Hundley W.G., Poldermans D., Bax J.J., Wegscheider K., Fleck E., Nagel E. Determination of interobserver variability for identifying inducible left ventricular wall motion abnormalities during dobutamine stress magnetic resonance imaging. Eur. Heart J. 2006, 27:1459-1464.
42. Pennell D.J. Cardiovascular magnetic resonance. Circulation 2010, 121:692-705.
43. Petitjean C., Dacher J.N. A review of segmentation methods in short axis cardiac MR images. Med. Image Anal. 2011, 15:169-184.
44. Radau P., Lu Y., Connelly K., Paul G., Dick A.J., Wright G.A. Evaluation framework for algorithms segmenting short axis cardiac MRI. MIDAS J. - Cardiac MR Left Ventricle Segment. Chall. 2009, 49.
45. Salton C.J., Chuang M.L., O'Donnell C.J., Kupka M.J., Larson M.G., Kissinger K.V., Edelman R.R., Levy D., Manning W.J. Gender differences and normal left ventricular anatomy in an adult population free of hypertension: a cardiovascular magnetic resonance study of the Framingham heart study offspring cohort. J. Am. Coll. Cardiol. 2002, 39:1055-1060.
46. Schaap M., Metz C.T., van Walsum T., van der Giessen A.G., Weustink A.C., Mollet N.R., Bauer C., Bogunović H., Castro C., Deng X., Dikici E., O'Donnell T., Frenay M., Friman O., Hernández Hoyos M., Kitslaar P.H., Krissian K., Kühnel C., Luengo-Oroz M.A., Orkisz M., Smedby O., Styner M., Szymczak A., Tek H., Wang C., Warfield S.K., Zambal S., Zhang Y., Krestin G.P., Niessen W.J. Standardized evaluation methodology and reference database for evaluating coronary artery centerline extraction algorithms. Med. Image Anal. 2009, 13:701-714.
47. Shattuck D.W., Prasad G., Mirza M., Narr K.L., Toga A.W. Online resource for validation of brain segmentation methods. Neuroimage 2009, 45:431-439.
48. Shi Y.Q., Sun H. Image and Video Compression for Multimedia Engineering 1999, CRC Press, Inc., Boca Raton, FL, USA. first ed.
49. Song B.C. A fast normalized cross correlation-based block matching algorithm using multilevel Cauchy-Schwartz inequality. ETRI J. 2011, 33:401-406.
50. Suinesiaputra A., Cowan B., Finn J., Fonseca C., Kadish A., Lee D., Medrano-Gracia P., Warfield S., Tao W., Young A. Left ventricular segmentation challenge from cardiac MRI: a collation study. Statistical Atlases and Computational Models of the Heart. Imaging and Modelling Challenges 2012, 88-97. Springer.
51. 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. Imag. 2004, 23:903-921.
52. Xing F., Soleimanifard S., Prince J.L., Landman B.A. Statistical fusion of continuous labels: identification of cardiac landmarks. Proc. Soc. Photo Opt. Instrum. Eng. 2011, 7962.
53. Young A.A., Cowan B.R., Thrupp S.F., Hedley W.J., Dell'Italia L.J. Left ventricular mass and volume: fast calculation with guide-point modeling on MR images. Radiology 2000, 216:597-602.
54. Zhu Y., Huang X., Wang W., Lopresti D., Long R., Antani S., Xue Z., Thoma G. Balancing the role of priors in multi-observer segmentation evaluation. J. Signal Process Syst. 2008, 55:185-207.