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Automatic Multi-Atlas Segmentation of Myocardium with SVF-Net

Part of the Lecture Notes in Computer Science book series (LNIP,volume 10663)


Segmentation of the myocardium is a key step for image guided diagnosis in many cardiac diseases. In this article, we propose an automatic multi-atlas segmentation framework which relies on a very fast registration algorithm trained with convolutional neural networks. The speed of this registration method allows us to use a high number of templates in the multi-atlas segmentation while remaining computationally tractable. The performance of the propose approach is evaluated on a dataset of 100 end-diastolic and end-systolic MRI images of the STACOM 2017 Automated Cardiac Diagnosis Challenge (ACDC).


  • Multi-atlas Segmentation (MAS)
  • Fully Convolutional Neural Network
  • Reference Deformation
  • Ground Truth Segmentation
  • Label Fusion Method

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  • DOI: 10.1007/978-3-319-75541-0_18
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  1. 1.


  1. Bulat, A., Tzimiropoulos, G.: Convolutional aggregation of local evidence for large pose face alignment. In: British Machine Vision Conference (2016)

    Google Scholar 

  2. Kilner, P.J., Geva, T., Kaemmerer, H., Trindade, P.T., Schwitter, J., Webb, G.D.: Recommendations for cardiovascular magnetic resonance in adults with congenital heart disease from the respective working groups of the European society of cardiology. Eur. Heart J. 31, 794–805 (2010). ehp586

    CrossRef  Google Scholar 

  3. Kramer, C.M., Barkhausen, J., Flamm, S.D., Kim, R.J., Nagel, E.: Standardized cardiovascular magnetic resonance (CMR) protocols 2013 update. J. Cardiovasc. Magn. Reson. 15(1), 91 (2013)

    CrossRef  Google Scholar 

  4. Lorenzi, M., Ayache, N., Frisoni, G.B., Pennec, X.: LCC-Demons: a robust and accurate symmetric diffeomorphic registration algorithm. NeuroImage 81, 470–483 (2013)

    CrossRef  Google Scholar 

  5. Payer, C., Štern, D., Bischof, H., Urschler, M.: Regressing heatmaps for multiple landmark localization using CNNs. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 230–238. Springer, Cham (2016).

    CrossRef  Google Scholar 

  6. Rohé, M.-M., Datar, M., Heimann, T., Sermesant, M., Pennec, X.: SVF-Net: learning deformable image registration using shape matching. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10433, pp. 266–274. Springer, Cham (2017).

    CrossRef  Google Scholar 

  7. Rohlfing, T., Brandt, R., Menzel, R., Maurer, C.R.: Evaluation of atlas selection strategies for atlas-based image segmentation with application to confocal microscopy images of bee brains. NeuroImage 21(4), 1428–1442 (2004)

    CrossRef  Google Scholar 

  8. Suinesiaputra, A., Bluemke, D.A., Cowan, B.R., Friedrich, M.G., Kramer, C.M., Kwong, R., Plein, S., Schulz-Menger, J., Westenberg, J.J., Young, A.A., et al.: Quantification of LV function and mass by cardiovascular magnetic resonance: multi-center variability and consensus contours. J. Cardiovasc. Magn. Reson. 17(1), 63 (2015)

    CrossRef  Google Scholar 

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Correspondence to Marc-Michel Rohé .

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Rohé, MM., Sermesant, M., Pennec, X. (2018). Automatic Multi-Atlas Segmentation of Myocardium with SVF-Net. In: , et al. Statistical Atlases and Computational Models of the Heart. ACDC and MMWHS Challenges. STACOM 2017. Lecture Notes in Computer Science(), vol 10663. Springer, Cham.

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