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International Conference on Artificial Intelligence and Security

ICAIS 2019: Artificial Intelligence and Security pp 3–11Cite as

CCNET: Cascading Convolutions for Cardiac Segmentation

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Part of the book series: Lecture Notes in Computer Science ((LNSC,volume 11633))

Abstract

Myocardial segmentation plays a pivotal role in the clinical diagnosis of cardiac diseases. The difference in size and shape of the heart poses an extensive challenge to the clinical diagnosis. Being specific, the large amount of noise generated by the cardiac magnetic resonance (CMR) images also gives rise to substantial interference in the clinical diagnosis. Inspired by associated tasks, we put forward a network for the myocardium segmentation. In the proposed methodology, at first, we establish numerous sub-sampling layers in a bid to attain the high-level features, together with fusing the feature information of different visual fields by assuming different convolution kernel sizes. Thereafter, high-level features coupled with initial input features are merged by means of a plurality of cascaded convolution layers. It is capable of directly improving the performance of myocardium segmentation. We perform an assessment of our approach on 165 CMR T1 mapping images with lower PSNR, and the results demonstrate that our architecture outperforms previous approaches.

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References

  1. Wang, J., et al.: Detecting cardiovascular disease from mammograms with deep learning. IEEE Trans. Med. Imaging PP(99), 1172–1181 (2017)

    Article  Google Scholar 

  2. Hauptmann, A., Arridge, S., Lucka, F., Muthurangu, V., Steeden, J.A.: Real-time cardiovascular MR with spatio-temporal de-aliasing using deep learning - proof of concept in congenital heart disease (2018)

    Google Scholar 

  3. Kelly, R.A., Balligand, J.L., Smith, T.W.: Nitric oxide and cardiac function. Life Sci. 81(10), 779–793 (1996)

    Google Scholar 

  4. Koch, W.J., et al.: Cardiac function in mice overexpressing the beta-adrenergic receptor kinase or a beta ark inhibitor. Science 268(5215), 1350–1353 (1995)

    Article  Google Scholar 

  5. Frustaci, A., et al.: Improvement in cardiac function in the cardiac variant of Fabry’s disease with galactose-infusion therapy. N. Engl. J. Med. 345(1), 25–32 (2001)

    Article  Google Scholar 

  6. Jadvar, H., Colletti, P.M.: Competitive advantage of PET/MRI. Eur. J. Radiol. 83(1), 84–94 (2014)

    Article  Google Scholar 

  7. Kim, Y.S., et al.: The advantage of high-resolution mri in evaluating basilar plaques: a comparison study with MRA. Atherosclerosis 224(2), 411–416 (2012)

    Article  Google Scholar 

  8. Lau, L.U., Thoeni, R.F.: Case report. Uterine lipoma: advantage of mri over ultrasound. Br. J. Radiol. 78(925), 72 (2005)

    Article  Google Scholar 

  9. Andica, C., et al.: The advantage of synthetic MRI for the visualization of early white matter change in an infant with sturge-weber syndrome. Magn. Reson. Med. Sc. 15(4), 347–348 (2016)

    Article  Google Scholar 

  10. Avendi, M.R., Kheradvar, A., Jafarkhani, H.: A combined deep-learning and deformable-model approach to fully automatic segmentation of the left ventricle in cardiac MRI. Med. Image Anal. 30, 108–119 (2016)

    Article  Google Scholar 

  11. Golkov, V., et al.: q-space deep learning for twelve-fold shorter and model-free diffusion MRI scans. IEEE Trans. Med. Imaging 35(5), 1344–1351 (2016)

    Article  Google Scholar 

  12. Beets-Tan, R.G.: MRI in rectal cancer: the T stage and circumferential resection margin. Colorectal Dis. 5(5), 392–395 (2010)

    Article  Google Scholar 

  13. Giedd, J.N., et al.: Brain development during childhood and adolescence: a longitudinal MRI study. Nat. Neurosci. 10(10), 861–863 (1999)

    Article  Google Scholar 

  14. Biswal, B., Yetkin, F.Z., Haughton, V.M., Hyde, J.S.: Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magn. Reson. Med. 34(4), 537–541 (2010)

    Article  Google Scholar 

  15. Luo, G., An, R., Wang, K., Dong, S., Zhang, H.: A deep learning network for right ventricle segmentation in short-axis MRI. In: Computing in Cardiology Conference (2017)

    Google Scholar 

  16. Kramer, C.M., Barkhausen, J., Flamm, S.D., Kim, R.J., Nagel, E.: Standardized cardiovascular magnetic resonance imaging (cmr) protocols, society for cardiovascular magnetic resonance: board of trustees task force on standardized protocols. J. Cardiovasc. Magn. Reson. 10(1), 35–35 (2008). Official Journal of the Society for Cardiovascular Magnetic Resonance

    Article  Google Scholar 

  17. Pennell, D.J., et al.: Clinical indications for cardiovascular magnetic resonance (CMR): consensus panel report. J. Cardiovasc. Magn. Reson. 25(21), 727–765 (2004)

    Article  Google Scholar 

  18. Moon, J.C., et al.: Myocardial T1 mapping and extracellular volume quantification: a society for cardiovascular magnetic resonance (SCMR) and CMR working group of the european society of cardiology consensus statement. J. Cardiovasc. Magn. Reson. 15(1), 92–92 (2013)

    Article  MathSciNet  Google Scholar 

  19. Singh, P., et al.: Cine-CMR partial voxel segmentation demonstrates increased aortic stiffness among patients with marfan syndrome. J. Thorac. Dis. 9(Suppl 4), S239 (2017)

    Article  Google Scholar 

  20. Arbelaez, P., Maire, M., Fowlkes, C., Malik, J.: Contour detection and hierarchical image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 33(5), 898–916 (2011)

    Article  Google Scholar 

  21. Yang, L., Zhang, Y., Chen, J., Zhang, S., Chen, D.Z.: Suggestive annotation: a deep active learning framework for biomedical image segmentation. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10435, pp. 399–407. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-66179-7_46

    Chapter  Google Scholar 

  22. Fang, S., et al.: Feature selection method based on class discriminative degree for intelligent medical diagnosis. CMC: Comput. Mater. Continua 55(3), 419–433 (2018)

    Google Scholar 

  23. Fang, W., Zhang, F., Sheng, V.S., Ding, Y.: A method for improving CNN-based image recognition using DCGAN. CMC: Comput. Mater. Continua 57(1), 167–178 (2018)

    Google Scholar 

  24. Charles, R.Q., Su, H., Mo, K., Guibas, L.J.: Pointnet: Deep learning on point sets for 3D classification and segmentation, pp. 77–85 (2016)

    Google Scholar 

  25. Wang, G., et al.: Interactive medical image segmentation using deep learning with image-specific fine-tuning. IEEE Trans. Med. Imaging PP(99) (2017)

    Google Scholar 

  26. Gaonkar, B., Hovda, D., Martin, N., Macyszyn, L.: Deep learning in the small sample size setting: cascaded feed forward neural networks for medical image segmentation. In: Medical Imaging 2016: Computer-Aided Diagnosis, p. 97852I (2016)

    Google Scholar 

  27. Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28

    Chapter  Google Scholar 

  28. Dong, H., Yang, G., Liu, F., Mo, Y., Guo, Y.: Automatic brain tumor detection and segmentation using U-Net based fully convolutional networks. In: Valdés Hernández, M., González-Castro, V. (eds.) MIUA 2017. CCIS, vol. 723, pp. 506–517. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-60964-5_44

    Chapter  Google Scholar 

  29. Tong, Q., Ning, M., Si, W., Liao, X., Qin, J.: 3D deeply-supervised U-Net based whole heart segmentation. In: Pop, M., et al. (eds.) STACOM 2017. LNCS, vol. 10663, pp. 224–232. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-75541-0_24

    Chapter  Google Scholar 

  30. Ronneberger, O.: Invited talk: U-Net convolutional networks for biomedical image segmentation. Bildverarbeitung für die Medizin 2017. I, p. 3. Springer, Heidelberg (2017). https://doi.org/10.1007/978-3-662-54345-0_3

    Chapter  Google Scholar 

  31. Basu, A., Buch, V., Vogels, W., Eicken, T.V.: U-Net: a user-level network interface for parallel and distributed computing. ACM Sigops Oper. Syst. Rev. 29(5), 40–53 (1995)

    Article  Google Scholar 

  32. Iglovikov, V., Shvets, A.: Ternausnet: U-net with vgg11 encoder pre-trained on imagenet for image segmentation (2018)

    Google Scholar 

  33. Brua, R.B., Culp, J.M., Benoy, G.A.: Comparison of benthic macroinvertebrate communities by two methods: Kick- and u-net sampling. Hydrobiologia 658(1), 293–302 (2011)

    Article  Google Scholar 

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Acknowledgment

This work was supported by the National Natural Science Foundation of China (Grant No. 61602066) and the Scientific Research Foundation (KYTZ201608) of CUIT and the major Project of Education Department in Sichuan (17ZA0063 and 2017JQ0030), and partially supported by the Sichuan international science and technology cooperation and exchange research program (2016HH0018).

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Authors and Affiliations

  1. Chengdu University of Information Technology, No. 24 Block 1, Xuefu Road, Chengdu, 610225, China

    Chao Luo, Xiaojie Li, Xi Wu, Jia He & Jiliu Zhou

  2. West China Hospital, Sichuan University, Chengdu, China

    Yucheng Chen

Authors
  1. Chao Luo
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  2. Xiaojie Li
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  3. Yucheng Chen
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  4. Xi Wu
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  5. Jia He
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  6. Jiliu Zhou
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Corresponding author

Correspondence to Xiaojie Li .

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Editors and Affiliations

  1. Nanjing University of Information Science and Technology, Nanjing, China

    Xingming Sun

  2. Nanjing University of Information Science and Technology, Nanjing, China

    Zhaoqing Pan

  3. Purdue University, West Lafayette, IN, USA

    Elisa Bertino

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Luo, C., Li, X., Chen, Y., Wu, X., He, J., Zhou, J. (2019). CCNET: Cascading Convolutions for Cardiac Segmentation. In: Sun, X., Pan, Z., Bertino, E. (eds) Artificial Intelligence and Security. ICAIS 2019. Lecture Notes in Computer Science(), vol 11633. Springer, Cham. https://doi.org/10.1007/978-3-030-24265-7_1

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  • DOI: https://doi.org/10.1007/978-3-030-24265-7_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-24264-0

  • Online ISBN: 978-3-030-24265-7

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