© The Institution of Engineering and Technology
This work conducts a feasibility study of deep learning approaches for automatic segmentation of left ventricle (LV) cavity from cardiac magnetic resonance (CMR) images. Automatic LV cavity segmentation is a challenging task, partially due to the small size of the object as compared to the large CMR image background, especially at the apex. To cater for small object segmentation, the authors present a localisation-segmentation framework, to first locate the object in the large full image, then segment the object within the small cropped region of interest. The localisation is performed by a deep regression model based on convolutional neural networks, while the segmentation is done by the deep neural networks based on U-Net architecture. They also employ the Dice loss function for the training process of the segmentation models, to investigate its effects on the segmentation performance. The deep learning models are trained and evaluated by using public endocardium-annotated CMR datasets from York University and MICCAI 2009 LV Challenge websites. The average dice metric values of the authors’ proposed framework are 0.91 and 0.93, respectively, on these two databases. These results are promising as compared to the best results achieved by the current state-of-art, which shows the potentials of deep learning approaches for this particular application.
References
-
-
1)
-
27. Long, J., Shelhamer, E., Darrell, T.: , 2015. .
-
2)
-
35. Li, Y., Qi, H., Dai, J., et al: , 2016. .
-
3)
-
22. van Tulder, G., de Bruijne, M.: ‘Learning features for tissue classification with the classification restricted Boltzmann machine’. MCV, 2014 (, 8848), pp. 47–58.
-
4)
-
18. Chen, T., Chefdhotel, C.: ‘Deep learning based automatic immune cell detection for immunohistochemistry images’. 5th Int. Workshop on Machine Learning in Medical Imaging (MLMI'14), Boston, MA, USA, 2014, pp. 17–24.
-
5)
-
26. Ngo, T.A., Carneiro, G.: ‘Left ventricle segmentation from cardiac MRI combining level set methods with deep belief networks’. IEEE Int. Conf. Image Processing (ICIP), Melbourne, Australia, 2013, pp. 695–699.
-
6)
-
30. Yang, X.L., Like, G., Yeo, S.Y., et al: ‘Automatic segmentation of left ventricular myocardium by deep convolutional and de-convolutional neural networks’. Computing in Cardiology (CINC 2016), 2016, pp. 81–84.
-
7)
-
20. Dhungel, N., Carneiro, G., Bradley, A.P.: , 2014. .
-
8)
-
2. Kang, D., Woo, J., Slomka, P.J., et al: ‘Heart chambers and whole heart segmentation techniques: review’, SPIE J. Electron. Imag., 2012, 21, (1), pp. 131–139.
-
9)
-
23. Greenspan, H., van Ginneken, B., Summers, R.M.: ‘Guest editorial – deep learning in medical imaging: overview and future promise of an exciting new techniques’, IEEE Trans. Med. Imag., 2016, 35, (5), pp. 1153–1159.
-
10)
-
14. Cruz-Roa, A., Basavanhally, A., Gonzalez, F., et al: ‘Automatic detection of invasive ductal carcinoma in whole slide images with convolutional neural networks’. SPIE Medical Imaging, vol. 9041, 2014, .
-
11)
-
21. Yang, X.L., Yeo, S.Y., Hong, J.M., et al: ‘A deep learning approach for tumor tissue image classification’. IASTED Biomedical Engineering, proceeding, , 2016, .
-
12)
-
28. Tran, P.V.: , 2016. .
-
13)
-
19. Dhungel, N., Carneiro, G., Bradley, A.P.: ‘Deep learning and structured prediction for the segmentation of mass in mammograms’. Medical Image Computing and Computer Assisted Intervention (MICCAI 2015), 2015 (, 9349), pp. 605–612.
-
14)
-
17. Esteva, A., Kuprel, B., Thrun, S.: ‘Deep networks for early stage skin disease and skin cancer classification’. , Stanford University, 2015.
-
15)
-
12. Ciresan, D.C., Giusti, A., Gambardella, L., et al: ‘Mitosis detection in breast cancer histology images with deep neural networks’. Medical Image Computing and Computer Assisted Interventions (MICCAI 2013), 2013 (, 8150), pp. 411–418.
-
16)
-
5. LeCun, Y., Bengio, Y., Hinton, G.: ‘Deep learning’, Nature, 2015, 521, pp. 436–444.
-
17)
-
41. Zheng, S., Jayasumana, S., Romera-Paredes, B., et al: , 2015. .
-
18)
-
24. Avendi, M.R., Kheradvar, A., Jafarkhani, H.: , 2015. .
-
19)
-
15. Cruz-Roa, A., Arevalo, J., Madabhushi, A., et al: ‘A deep learning architecture for image representation, visual interpretability and automated basal-cell carcinoma cancer detection’. Medical Image Computing and Computer-Assisted Intervention (MICCAI 2013), 2013 (, 8150), pp. 403–410.
-
20)
-
39. Noh, H., Hong, S., Han, B.: , 2015. .
-
21)
-
16. Ciresan, D.C., Giusti, A., Gambardella, L.M., et al: ‘Deep neural networks segment neuronal membranes in electron microscopy images’. Neural Information Processing Systems (NIPS 2012), 2012, pp. 2843–2851.
-
22)
-
47. Korlev, S., Safiullin, A., Belyaev, M., et al: , 2017. .
-
23)
-
4. Bengio, Y., Courville, A., Vincent, P.: ‘Representation learning: a review and new perspectives’, IEEE Trans. Pattern Anal. Mach. Intell., 2013, 35, (8), pp. 1798–2828.
-
24)
-
33. Andreopoulos, A., Tsotsos, J.K.: ‘Efficient and generalizable statistical models of shape and appearance for analysis of cardiac MRI’, Med. Image Anal., 2008, 12, (3), pp. 335–357. .
-
25)
-
8. Krizhevsky, A., Sutskever, I., Hinton, G.E.: ‘ImageNet classification with deep convolutional neural networks’. Neural Information Processing Systems (NIPS 2012), 2012, pp. 1106–1114.
-
26)
-
32. Drozdzal, M., Vorontsov, E., Chartrand, G., et al: , 2016. .
-
27)
-
43. U-Net Kera Implementation, 2016. .
-
28)
-
1. Petitjean, C., Dacher, J.N.: ‘A review of segmentation methods in short axis cardiac MR images’, Med. Image Anal., 2011, 15, (2), pp. 169–184.
-
29)
-
25. Emad, O., Yassine, I.A., Fahmy, A.S.: ‘Automatic localization of the left ventricle in cardiac MRI images using deep learning’. IEEE Engineering in Medicine and Biology Society (EMBC), Milan, Italy, 2015, pp. 683–686.
-
30)
-
11. He, K., Zhang, X., Ren, S., et al: , 2015. .
-
31)
-
13. Malon, C., Cosatto, E.: ‘Classification of mitotic figures with convolutional neural networks and seeded blob features’, J. Pathol. Inf., 2013, 4, (1), p. 9.
-
32)
-
7. LeCun, Y., Bottou, L., Bengio, Y., et al: ‘Gradient-based learning applied to document recognition’, Proc. IEEE, 1998, 86, (11), pp. 2278–2324.
-
33)
-
40. Chen, L.C., Papandreou, G., Kokkinos, I., et al: , 2014. .
-
34)
-
10. Szegedy, C., Liu, W., Jia, Y., et al: , 2014. .
-
35)
-
37. Petitjean, C., Zuluaga, M.A., Bai, W., et al: ‘Right ventricle segmentation from cardiac MRI: a collation study’, Med. Image Anal., 2015, 19, (1), pp. 287–202.
-
36)
-
46. Kleesiek, J., Urban, G., Hubert, A., et al: ‘Deep MRI brain extraction: a 3D convolutional neural networks for skull stripping’, NeuroImage, 2016, 129, pp. 460–469.
-
37)
-
45. Kingma, D., Ba, J.: , 2014. .
-
38)
-
9. Simonyan, K., Zisserman, A.: , 2014. .
-
39)
-
36. He, K., Gkioxari, G., Dollar, P., et al: , 2017. .
-
40)
-
3. Cocosco, C., Niessen, W., Netsch, T., et al: ‘Automatic image- driven segmentation of the ventricles in cardiac cine MRI’, J. Magn. Reson. Imag., 2008, 28, (2), pp. 366–374.
-
41)
-
6. Schmidhuber, J.: ‘Deep learning in neural networks: an overview’, Neural Netw., 2015, 61, pp. 85–117.
-
42)
-
29. Ronneberger, O., Fischer, P., Brox, T.: , 2015. .
-
43)
-
38. Yang, X.L., Su, Y., Duan, R., et al: ‘Cardiac image segmentation by random walks with dynamic information’, IET Comput. Vis., 2016, 10, (1), pp. 79–86.
-
44)
-
34. Radau, P., Lu, Y., Connelly, K., et al: ‘Evaluation framework for algorithms segmenting short axis cardiac MRI’. MICCAI 2009 Workshop on Cardiac MR Left Ventricle Segmentation Challenge, 2009. .
-
45)
-
42. Yu, F., Koltun, V.: , 2015. .
-
46)
-
31. Milletari, F., Navab, N., Ahmadi, S.A.: , 2016. .
-
47)
-
44. Deep Learning for Cardiac Segmentation, 2017. .
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