Multi-level Activation for Segmentation of Hierarchically-Nested Classes

  • Marie PiraudEmail author
  • Anjany Sekuboyina
  • Björn H. Menze
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11134)


For many biological image segmentation tasks, including topological knowledge, such as the nesting of classes, can greatly improve results. However, most ‘out-of-the-box’ CNN models are still blind to such prior information. In this paper, we propose a novel approach to encode this information, through a multi-level activation layer and three compatible losses. We benchmark all of them on nuclei segmentation in bright-field microscopy cell images from the 2018 Data Science Bowl challenge, offering an exemplary segmentation task with cells and nested subcellular structures. Our scheme greatly speeds up learning, and outperforms standard multi-class classification with soft-max activation and a previously proposed method stemming from it, improving the Dice score significantly (p-values \(<0.007\)). Our approach is conceptually simple, easy to implement and can be integrated in any CNN architecture. It can be generalized to a higher number of classes, with or without further relations of containment.


Segmentation Multiclass Inclusion Nested classes Class hierarchy 

Supplementary material

478828_1_En_24_MOESM1_ESM.pdf (316 kb)
Supplementary material 1 (pdf 315 KB)


  1. 1.
    Nosrati, M.S., Hamarneh, G.: Local optimization based segmentation of spatially-recurring, multi-region objects with part configuration constraints. IEEE Trans. Med. Imaging 33(9), 1845–1859 (2014)CrossRefGoogle Scholar
  2. 2.
    BenTaieb, A., Hamarneh, G.: Topology aware fully convolutional networks for histology gland segmentation. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 460–468. Springer, Cham (2016). Scholar
  3. 3.
    Christ, P.F., et al.: Automatic liver and lesion segmentation in CT using cascaded fully convolutional neural networks and 3D conditional random fields. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 415–423. Springer, Cham (2016). Scholar
  4. 4.
    Fidon, L., Li, W., Garcia-Peraza-Herrera, L.C., Ekanayake, J., Kitchen, N., Ourselin, S., Vercauteren, T.: Generalised wasserstein dice score for imbalanced multi-class segmentation using holistic convolutional networks. In: Crimi, A., Bakas, S., Kuijf, H., Menze, B., Reyes, M. (eds.) BrainLes 2017. LNCS, vol. 10670, pp. 64–76. Springer, Cham (2018). Scholar
  5. 5.
    Bauer, S., Tessier, J., Krieter, O., Nolte, L.-P., Reyes, M.: Integrated spatio-temporal segmentation of longitudinal brain tumor imaging studies. In: Menze, B., Langs, G., Montillo, A., Kelm, M., Müller, H., Tu, Z. (eds.) MCV 2013. LNCS, vol. 8331, pp. 74–83. Springer, Cham (2014). Scholar
  6. 6.
    Alberts, E., Charpiat, G., Tarabalka, Y., Huber, T., Weber, M.-A., Bauer, J., Zimmer, C., Menze, B.H.: A nonparametric growth model for brain tumor segmentation in longitudinal MR sequences. In: Crimi, A., Menze, B., Maier, O., Reyes, M., Handels, H. (eds.) BrainLes 2015. LNCS, vol. 9556, pp. 69–79. Springer, Cham (2016). Scholar
  7. 7.
    Liu, Z., Li, X., Luo, P., et al.: Deep learning Markov random field for semantic segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 8828(c), 1 (2017)Google Scholar
  8. 8.
    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). Scholar
  9. 9.
    Bhattacharyya, S., Dutta, P., Nandi, P.K.: Multilevel activation functions for true color image segmentation using a self supervised parallel self organizing neural network (PSONN) architecture: a comparative study. Int. J. Comput. Sci. 1(8), 2452–2464 (2007)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Marie Piraud
    • 1
    Email author
  • Anjany Sekuboyina
    • 1
  • Björn H. Menze
    • 1
  1. 1.Department of Computer ScienceTechnische Universität MünchenMunichGermany

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