Multi-scale deep networks and regression forests for direct bi-ventricular volume estimation

Medical Image Analysis

Volumn 30, Issue , 2016, Pages 120-129

  Zhen, Xiantong ^a,b   Wang, Zhijie ^c   Islam, Ali ^d   Bhaduri, Mousumi ^e   Chan, Ian ^e   Li, Shuo ^a,b,c  

^a UNIVERSITY OF WESTERN ONTARIO   (Canada)

^b Digital Image Group (DIG)   (Canada)

^c GE HEALTHCARE   (Canada)

^d ST JOSEPH S HEALTH CENTRE   (Canada)

^e London Healthcare Sciences Centre   (Canada)

Author keywords

Direct volume estimation; Multi scale deep networks; Random forests; Regression

Indexed keywords

DECISION TREES; RANDOM FORESTS;

CARDIAC FUNCTION ANALYSIS; CARDIAC IMAGES; CROSS VALIDATION; EFFECTIVENESS AND EFFICIENCIES; REGRESSION; REGRESSION FORESTS; RIGHT VENTRICLE; VOLUME ESTIMATIONS;

HEART;

ARTICLE; BIVENTRICULAR ASSIST DEVICE; CARDIAC IMAGING; COMPUTER NETWORK; CONCEPTUAL FRAMEWORK; CONTROLLED STUDY; DISCRIMINANT ANALYSIS; EXPERT SYSTEM; HEART LEFT VENTRICLE; HEART RIGHT VENTRICLE; HEART VOLUME; HUMAN; JACKKNIFE TEST; PRIORITY JOURNAL; RANDOM FOREST; REGRESSION ANALYSIS; ALGORITHM; ARTIFICIAL NEURAL NETWORK; AUTOMATED PATTERN RECOGNITION; COMPUTER ASSISTED DIAGNOSIS; DIAGNOSTIC IMAGING; HEART STROKE VOLUME; HEART VENTRICLE; HEART VENTRICLE FUNCTION; IMAGE ENHANCEMENT; MACHINE LEARNING; ORGAN SIZE; PROCEDURES; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY; THREE DIMENSIONAL IMAGING;

ALGORITHMS; HEART VENTRICLES; HUMANS; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; IMAGING, THREE-DIMENSIONAL; MACHINE LEARNING; NEURAL NETWORKS (COMPUTER); ORGAN SIZE; PATTERN RECOGNITION, AUTOMATED; REGRESSION ANALYSIS; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; STROKE VOLUME; VENTRICULAR DYSFUNCTION;

EID: 84958981335     PISSN: 13618415     EISSN: 13618423     Source Type: Journal    
DOI: 10.1016/j.media.2015.07.003     Document Type: Article

References (62)

1. Afshin M., Ayed I.B., Islam A., Goela A., Peters T.M., Li S. Global assessment of cardiac function using image statistics in MRI. Medical Image Computing and Computer-Assisted Intervention-MICCAI 2012 2012, 535-543.
2. Afshin M., Ben Ayed I., Punithakumar K., Law M., Islam A., Goela A., Peters T., Li S. Regional assessment of cardiac left ventricular myocardial function via MRI statistical features. IEEE Trans. Med. Imag 2014, 33(2):481-494.
3. Afshin M., Ben Ayed I., Reza Sadeghi Neshat H., Punithakumar K., Goela A., Islam A., Peters T., Li S. Automated assessment of regional left ventricular function for cardiac MRI with minimal user interaction. Annual Meeting - Radiological Society of North America (RSNA) 2012.
4. Ayed B.I., Li S., Ross I. Embedding overlap priors in variational left ventricle tracking. IEEE Trans. Med. Imag. 2009, 28(12):1902-1913.
5. Ayed B.I., Punithakumar K., Li S., Islam A., Chong J. Left ventricle segmentation via graph cut distribution matching. Medical Image Computing and Computer-Assisted Intervention-MICCAI 2009 2009, 901-909. Springer.
6. Bengio Y., Courville A., Vincent P. Representation learning: A review and new perspectives. IEEE Trans. Patt. Anal. Machi. Intell. 2013, 35:1798-1828.
7. Bengio Y., Lamblin P., Popovici D., Larochelle H., et al. Greedy layer-wise training of deep networks. Advances in neural information processing systems 2007, 19:153.
8. Biau G. Analysis of a random forests model. J. Mach. Learn. Res. 2012, 13(1):1063-1095.
9. Bolte H., Jahnke T., Schäfer F., Wenke R., Hoffmann B., Freitag-Wolf S., Dicken V., Kuhnigk J., Lohmann J., Voss S., et al. Interobserver-variability of lung nodule volumetry considering different segmentation algorithms and observer training levels. Eur. J. Radiol. 2007, 64(2):285-295.
10. Breiman L. Random forests. Mach. Learn. 2001, 45(1):5-32.
11. Burt P.J., Adelson E.H. The Laplacian pyramid as a compact image code. IEEE Trans. Commun. 1983, 31(4):532-540.
12. Carneiro G., Nascimento J. Combining multiple dynamic models and deep learning architectures for tracking the left ventricle endocardium in ultrasound data. IEEE Trans. Patt. Anal. Mach. Intell 2013, 35(11):2592-2607.
13. Cerqueira M.D., Weissman N.J., Dilsizian V., Jacobs A.K., Kaul S., Laskey W.K., Pennell D.J., Rumberger J.A., Ryan T., Verani M.S., et al. Standardized myocardial segmentation and nomenclature for tomographic imaging of the heart a statement for healthcare professionals from the cardiac imaging committee of the council on clinical cardiology of the american heart association. Circulation 2002, 105(4):539-542.
14. Chen T., Wang X., Chung S., Metaxas D., Axel L. Automated 3d motion tracking using gabor filter bank, robust point matching, and deformable models. IEEE Trans. Med. Imag. 2010, 29(1):1-11.
15. Cireşan D.C., Giusti A., Gambardella L.M., Schmidhuber J. Mitosis detection in breast cancer histology images with deep neural networks. Medical Image Computing and Computer-Assisted Intervention-MICCAI 2013 2013, 411-418. Springer.
16. Cocosco C.A., Niessen W.J., Netsch T., Vonken E.-j., Lund G., Stork A., Viergever M.A. Automatic image-driven segmentation of the ventricles in cardiac cine mri. J. Magn. Reson. Imag. 2008, 28(2):366-374.
17. Criminisi A., Shotton J. Decision Forests for Computer Vision and Medical Image Analysis 2013, Springer Publishing Company, Incorporated.
18. Criminisi A., Shotton J., Robertson D., Konukoglu E. Regression forests for efficient anatomy detection and localization in ct studies. MICCAI Workshop on Medical Computer Vision 2011, 106-117.
19. Dalal N., Triggs B. Histograms of oriented gradients for human detection. IEEE Computer Society Conference on Computer Vision and Pattern Recognition 2005, vol. 1:886-893.
20. Fritz D., Rinck D., Dillmann R., Scheuering M. Segmentation of the left and right cardiac ventricle using a combined bi-temporal statistical model. Medical Imaging 2006, 614121. International Society for Optics and Photonics.
21. Gall J., Yao A., Razavi N., Van Gool L., Lempitsky V. Hough forests for object detection, tracking, and action recognition. IEEE Trans. Patt. Anal. Mach. Intell. 2011, 33(11):2188-2202.
22. Garcia-Barnes J., Gil D., Badiella L., Hernandez-Sabate A., Carreras F., Pujades S., Martí E. A normalized framework for the design of feature spaces assessing the left ventricular function. IEEE Trans. Med. Imag. 2010, 29(3):733-745.
23. Heckel F., Meine H., Moltz J., Kuhnigk J., Heverhagen J., Kiebling A., Buerke B., Hahn H. Segmentation-based partial volume correction for volume estimation of solid lesions in ct. IEEE Trans. Med. Imag. 2014, 22:462-480.
24. Hinton G. A practical guide to training restricted boltzmann machines. Momentum 2010, 9(1):926.
25. Hinton G.E. Training products of experts by minimizing contrastive divergence. Neural Comput. 2002, 14(8):1771-1800.
26. Hinton G.E., Osindero S., Teh Y.-W. A fast learning algorithm for deep belief nets. Neural Comput. 2006, 18(7):1527-1554.
27. Hu Z., Metaxas D., Axel L. Computational modeling and simulation of heart ventricular mechanics from tagged MRI. Func. Imag. Model. Heart 2005, 881-883.
28. Huang D., Storer M., De la Torre F., Bischof H. Supervised local subspace learning for continuous head pose estimation. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2011, 2921-2928. IEEE.
29. Krizhevsky A., Sutskever I., Hinton G.E. Imagenet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems 2012.
30. LeCun Y., Bottou L., Bengio Y., Haffner P. Gradient-based learning applied to document recognition. Proceedings of the IEEE 1998, 86(11):2278-2324.
31. Lee H., Ekanadham C., Ng A.Y. Sparse deep belief net model for visual area v2. Advances in Neural Information Processing Systems 2008, 873-880.
32. Lee H., Grosse R., Ranganath R., Ng A.Y. Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations. Proceedings of the 26th Annual International Conference on Machine Learning 2009, 609-616. ACM.
33. Lötjönen J., Kivistö S., Koikkalainen J., Smutek D., Lauerma K. Statistical shape model of atria, ventricles and epicardium from short-and long-axis mr images. Med. Imag. Anal. 2004, 8(3):371-386.
34. Lu X., Wang Y., Georgescu B., Littman A., Comaniciu D. Automatic delineation of left and right ventricles in cardiac mri sequences using a joint ventricular model. Functional Imaging and Modeling of the Heart 2011, 250-258. Springer.
35. Marchesseau S., Delingette H., Sermesant M., Cabrera-Lozoya R., Tobon-Gomez C., Moireau P., Figueras i Ventura R., Lekadir K., Hernandez A., Garreau M., et al. Personalization of a cardiac electromechanical model using reduced order unscented kalman filtering from regional volumes. Med. Imag. Anal. 2013, 17(7):816-829.
36. Montillo A., Metaxas D., Axel L. Extracting tissue deformation using gabor filter banks. Medical Imaging 2004 2004, 1-9. International Society for Optics and Photonics.
37. Petersen K., Nielsen M., Diao P., Karssemeijer N., Lillholm M. Breast tissue segmentation and mammographic risk scoring using deep learning. Breast Imaging 2014, 88-94. Springer.
38. Petitjean C., Dacher J.-N. A review of segmentation methods in short axis cardiac mr images. Med. Imag. Anal. 2011, 15(2):169-184.
39. Poultney C., Chopra S., Cun Y.L., et al. Efficient learning of sparse representations with an energy-based model. Advances in Neural Information Processing Systems 2006, 1137-1144.
40. Prasoon A., Petersen K., Igel C., Lauze F., Dam E., Nielsen M. Deep feature learning for knee cartilage segmentation using a triplanar convolutional neural network. Medical Image Computing and Computer-Assisted Intervention-MICCAI 2013 2013, 246-253. Springer.
41. Punithakumar K., Ben Ayed I., Islam A., Goela A., Ross I.G., Chong J., Li S. Regional heart motion abnormality detection: An information theoretic approach. Med. Imag. Anal. 2013, 17(3):311-324.
42. Qian Z., Metaxas D.N., Axel L. Extraction and tracking of mri tagging sheets using a 3d gabor filter bank. Engineering in Medicine and Biology Society, 2006. EMBS'06. 28th Annual International Conference of the IEEE 2006, 711-714. IEEE.
43. Shao L., Zhen X., Tao D., Li X. Spatio-temporal laplacian pyramid coding for action recognition. IEEE Trans. Cybernetics 2014, 44(6):817-827.
44. Shin H.-C., Orton M.R., Collins D.J., Doran S.J., Leach M.O. Stacked autoencoders for unsupervised feature learning and multiple organ detection in a pilot study using 4d patient data. IEEE Trans. Patt. Anal. Mach. Intell. 2013, 35(8):1930-1943.
45. Shotton J., Sharp T., Kipman A., Fitzgibbon A., Finocchio M., Blake A., Cook M., Moore R. Real-time human pose recognition in parts from single depth images. Commun. ACM 2013, 56(1):116-124.
46. Tang J., Shao L., Zhen X. Robust point pattern matching based on spectral context. Patt. Recognit. 2014, 47(3):1469-1484.
47. Turaga S.C., Murray J.F., Jain V., Roth F., Helmstaedter M., Briggman K., Denk W., Seung H.S. Convolutional networks can learn to generate affinity graphs for image segmentation. Neural Comput. 2010, 22(2):511-538.
48. Wang H., Amini A.A. Cardiac motion and deformation recovery from mri: a review. IEEE Trans. Med. Imag. 2012, 31(2):487-503.
49. Wang S., Summers R.M. Machine learning and radiology. Med. Imag. Anal. 2012, 16(5):933-951.
50. Wang V.Y., Lam H., Ennis D.B., Cowan B.R., Young A.A., Nash M.P. Modelling passive diastolic mechanics with quantitative mri of cardiac structure and function. Med. Imag. Anal. 2009, 13(5):773-784.
51. Wang Z., Ben Salah M., Gu B., Islam A., Goela A., Li S. Direct estimation of cardiac bi-ventricular volumes with an adapted bayesian formulation. IEEE Trans. Biomed. Eng. 2014, 61(4):1251-1260.
52. Wang Z., Salah M., Ayed I., Islam A., Goela A., Li S. Bi-ventricular volume estimation for cardiac functional assessment. Annual Meeting - Radiological Society of North America (RSNA) 2013.
53. Zettinig O., Mansi T., Neumann D., Georgescu B., Rapaka S., Seegerer P., Kayvanpour E., Sedaghat-Hamedani F., Amr A., Haas J., et al. Data-driven estimation of cardiac electrical diffusivity from 12-lead ecg signals. Med. Imag. Anal 2014, 18(8):1361-1376.
54. Zhen X., Shao L. A local descriptor based on laplacian pyramid coding for action recognition. Patt. Recognit. Lett. 2013, 34(15):1899-1905.
55. Zhen X., Shao L., Li X. Action recognition by spatio-temporal oriented energies. Inf. Sci. 2014, 281:295-309.
56. Zhen X., Shao L., Tao D., Li X. Embedding motion and structure features for action recognition. IEEE Trans. Circuits Syst. Video Technol. 2013, 23(7):1182-1190.
57. Zhen X., Shao L., Zheng F. Discriminative embedding via image-to-class distances. British Machine Vision Conference 2014.
58. Zhen X., Wang Z., Islam A., Bhaduri M., Chan I., Li S. Direct volume estimation without segmentation. SPIE Medical Imaging 2015, 94132G. International Society for Optics and Photonics.
59. Zhen X., Wang Z., Islam A., Chan I., Li S. A comparative study of methods for cardiac ventricular volume estimation. Annual Meeting - Radiological Society of North America (RSNA) 2014.
60. Zhen X., Wang Z., Islam A., Chan I., Li S. Direct estimation of cardiac bi-ventricular volumes with regression forests. Accepted by Medical Image Computing and Computer-Assisted Intervention-MICCAI 2014 2014.
61. Zhen X., Wang Z., Yu M., Li S. Supervised descriptor learning for multi-output regression. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2015.
62. Zheng Y., Barbu A., Georgescu B., Scheuering M., Comaniciu D. Four-chamber heart modeling and automatic segmentation for 3-d cardiac ct volumes using marginal space learning and steerable features. IEEE Trans. Med. Imag. 2008, 27(11):1668-1681.