Skip to main content

2016 | OriginalPaper | Buchkapitel

Deep Fusion Net for Multi-atlas Segmentation: Application to Cardiac MR Images

verfasst von : Heran Yang, Jian Sun, Huibin Li, Lisheng Wang, Zongben Xu

Erschienen in: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016

Verlag: Springer International Publishing

Aktivieren Sie unsere intelligente Suche um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

Atlas selection and label fusion are two major challenges in multi-atlas segmentation. In this paper, we propose a novel deep fusion net for better solving these challenges. Deep fusion net is a deep architecture by concatenating a feature extraction subnet and a non-local patch-based label fusion (NL-PLF) subnet in a single network. This network is trained end-to-end for automatically learning deep features achieving optimal performance in a NL-PLF framework. The learned deep features are further utilized in defining a similarity measure for atlas selection. Experimental results on Cardiac MR images for left ventricular segmentation demonstrate that our approach is effective both in atlas selection and multi-atlas label fusion, and achieves state of the art in performance.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Literatur
1.
Zurück zum Zitat Bai, W., Shi, W., Ledig, C., Rueckert, D.: Multi-atlas segmentation with augmented features for cardiac mr images. Med. Image Anal. 19(1), 98–109 (2015)CrossRef Bai, W., Shi, W., Ledig, C., Rueckert, D.: Multi-atlas segmentation with augmented features for cardiac mr images. Med. Image Anal. 19(1), 98–109 (2015)CrossRef
2.
Zurück zum Zitat Coupé, P., Manjón, J.V., Fonov, V., Pruessner, J., Robles, M., Collins, D.L.: Patch-based segmentation using expert priors: application to hippocampus and ventricle segmentation. NeuroImage 54(2), 940–954 (2011)CrossRef Coupé, P., Manjón, J.V., Fonov, V., Pruessner, J., Robles, M., Collins, D.L.: Patch-based segmentation using expert priors: application to hippocampus and ventricle segmentation. NeuroImage 54(2), 940–954 (2011)CrossRef
3.
Zurück zum Zitat Dhungel, N., Carneiro, G., Bradley, A.P.: Deep learning and structured prediction for the segmentation of mass in mammograms. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9349, pp. 605–612. Springer, Heidelberg (2015). doi:10.1007/978-3-319-24553-9_74 CrossRef Dhungel, N., Carneiro, G., Bradley, A.P.: Deep learning and structured prediction for the segmentation of mass in mammograms. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9349, pp. 605–612. Springer, Heidelberg (2015). doi:10.​1007/​978-3-319-24553-9_​74 CrossRef
4.
Zurück zum Zitat Duc, A.K.H., Modat, M., Leung, K.K., Cardoso, M.J., Barnes, J., Kadir, T., Ourselin, S.: Using manifold learning for atlas selection in multi-atlas segmentation. PloS one 8(8), e70059 (2013)CrossRef Duc, A.K.H., Modat, M., Leung, K.K., Cardoso, M.J., Barnes, J., Kadir, T., Ourselin, S.: Using manifold learning for atlas selection in multi-atlas segmentation. PloS one 8(8), e70059 (2013)CrossRef
5.
Zurück zum Zitat Giraud, R., Ta, V.T., Papadakis, N., Manjón, J.V., Collins, D.L., Coupé, P.: An optimized patchmatch for multi-scale and multi-feature label fusion. NeuroImage 124, 770–782 (2016)CrossRef Giraud, R., Ta, V.T., Papadakis, N., Manjón, J.V., Collins, D.L., Coupé, P.: An optimized patchmatch for multi-scale and multi-feature label fusion. NeuroImage 124, 770–782 (2016)CrossRef
6.
Zurück zum Zitat Iglesias, J.E., Sabuncu, M.R.: Multi-atlas segmentation of biomedical images: a survey. Med. Image Anal. 24(1), 205–219 (2015)CrossRef Iglesias, J.E., Sabuncu, M.R.: Multi-atlas segmentation of biomedical images: a survey. Med. Image Anal. 24(1), 205–219 (2015)CrossRef
7.
Zurück zum Zitat Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105. Curran Associates, Inc. (2012) Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105. Curran Associates, Inc. (2012)
8.
Zurück zum Zitat Roth, H.R., Lu, L., Farag, A., Shin, H.-C., Liu, J., Turkbey, E.B., Summers, R.M.: DeepOrgan: multi-level deep convolutional networks for automated pancreas segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9349, pp. 556–564. Springer, Heidelberg (2015). doi:10.1007/978-3-319-24553-9_68 CrossRef Roth, H.R., Lu, L., Farag, A., Shin, H.-C., Liu, J., Turkbey, E.B., Summers, R.M.: DeepOrgan: multi-level deep convolutional networks for automated pancreas segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9349, pp. 556–564. Springer, Heidelberg (2015). doi:10.​1007/​978-3-319-24553-9_​68 CrossRef
9.
Zurück zum Zitat Sanroma, G., Wu, G., Gao, Y., Shen, D.: Learning to rank atlases for multiple-atlas segmentation. IEEE Trans. Med. Imaging 33(10), 1939–1953 (2014)CrossRef Sanroma, G., Wu, G., Gao, Y., Shen, D.: Learning to rank atlases for multiple-atlas segmentation. IEEE Trans. Med. Imaging 33(10), 1939–1953 (2014)CrossRef
10.
Zurück zum Zitat Shi, W., et al.: Cardiac image super-resolution with global correspondence using multi-atlas patchmatch. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds.) MICCAI 2013. LNCS, vol. 8151, pp. 9–16. Springer, Heidelberg (2013). doi:10.1007/978-3-642-40760-4_2 CrossRef Shi, W., et al.: Cardiac image super-resolution with global correspondence using multi-atlas patchmatch. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds.) MICCAI 2013. LNCS, vol. 8151, pp. 9–16. Springer, Heidelberg (2013). doi:10.​1007/​978-3-642-40760-4_​2 CrossRef
11.
Zurück zum Zitat Tong, T., Wolz, R., Wang, Z., Gao, Q., Misawa, K., Fujiwara, M., Mori, K., Hajnal, J.V., Rueckert, D.: Discriminative dictionary learning for abdominal multi-organ segmentation. Med. Image Anal. 23(1), 92–104 (2015)CrossRef Tong, T., Wolz, R., Wang, Z., Gao, Q., Misawa, K., Fujiwara, M., Mori, K., Hajnal, J.V., Rueckert, D.: Discriminative dictionary learning for abdominal multi-organ segmentation. Med. Image Anal. 23(1), 92–104 (2015)CrossRef
Metadaten
Titel
Deep Fusion Net for Multi-atlas Segmentation: Application to Cardiac MR Images
verfasst von
Heran Yang
Jian Sun
Huibin Li
Lisheng Wang
Zongben Xu
Copyright-Jahr
2016
DOI
https://doi.org/10.1007/978-3-319-46723-8_60