Weakly Supervised Segmentation by a Deep Geodesic Prior

  • Aliasghar MortaziEmail author
  • Naji Khosravan
  • Drew A. Torigian
  • Sila Kurugol
  • Ulas Bagci
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11861)


The performance of the state-of-the-art image segmentation methods heavily relies on the high-quality annotations, which are not easily affordable, particularly for medical data. To alleviate this limitation, in this study, we propose a weakly supervised image segmentation method based on a deep geodesic prior. We hypothesize that integration of this prior information can reduce the adverse effects of weak labels in segmentation accuracy. Our proposed algorithm is based on a prior information, extracted from an auto-encoder, trained to map objects’ geodesic maps to their corresponding binary maps. The obtained information is then used as an extra term in the loss function of the segmentor. In order to show efficacy of the proposed strategy, we have experimented segmentation of cardiac substructures with clean and two levels of noisy labels (L1, L2). Our experiments showed that the proposed algorithm boosted the performance of baseline deep learning-based segmentation for both clean and noisy labels by \(4.4\%\), \(4.6\%\)(L1), and \(6.3\%\)(L2) in dice score, respectively. We also showed that the proposed method was more robust in the presence of high-level noise due to the existence of shape priors.


Medical image segmentation Deep learning Shape prior Weakly supervised Geodesic prior 


  1. 1.
    Cardiovascular Diseases (CVDs) (2007). Accessed 30 June 2017
  2. 2.
    Badrinarayanan, V., Kendall, A., Cipolla, R.: SegNet: a deep convolutional encoder-decoder architecture for image segmentation. arXiv preprint arXiv:1511.00561 (2015)
  3. 3.
    Bernard, O., et al.: Deep learning techniques for automatic mri cardiac multi-structures segmentation and diagnosis: is the problem solved? IEEE Trans. Med. Imaging 37(11), 2514–2525 (2018)CrossRefGoogle Scholar
  4. 4.
    Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: CVPR, vol. 1, p. 3 (2017)Google Scholar
  5. 5.
    Jégou, S., Drozdzal, M., Vazquez, D., Romero, A., Bengio, Y.: The one hundred layers Tiramisu: fully convolutional DenseNets for semantic segmentation. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 1175–1183. IEEE (2017)Google Scholar
  6. 6.
    Kurugol, S., Ozay, N., Dy, J.G., Sharp, G.C., Brooks, D.H.: Locally deformable shape model to improve 3D level set based esophagus segmentation. In: 2010 20th International Conference on Pattern Recognition (ICPR). IEEE (2010)Google Scholar
  7. 7.
    LaLonde, R., Bagci, U.: Capsules for object segmentation. In: MIDL Conference, ArXiv preprint arXiv:1804.04241 (2018)
  8. 8.
    Lim, P.H., Bagci, U., Bai, L.: A new prior shape model for level set segmentation. In: San Martin, C., Kim, S.-W. (eds.) CIARP 2011. LNCS, vol. 7042, pp. 125–132. Springer, Heidelberg (2011). Scholar
  9. 9.
    Oktay, O., et al.: Anatomically constrained neural networks (ACNNs): application to cardiac image enhancement and segmentation. IEEE Trans. Med. Imaging 37(2), 384–395 (2018)CrossRefGoogle Scholar
  10. 10.
    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
  11. 11.
    Telea, A.: An image inpainting technique based on the fast marching method. J. Graph. Tools 9(1), 23–34 (2004)CrossRefGoogle Scholar
  12. 12.
    Zotti, C., Luo, Z., Lalande, A., Jodoin, P.M.: Convolutional neural network with shape prior applied to cardiac MRI segmentation. IEEE J. Biomed. Health Inform. 23, 1119–1128 (2018)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Aliasghar Mortazi
    • 1
    • 2
    • 3
    Email author
  • Naji Khosravan
    • 1
  • Drew A. Torigian
    • 2
  • Sila Kurugol
    • 3
  • Ulas Bagci
    • 1
  1. 1.Center for Research in Computer Vision (CRCV), School of Computer ScienceUniversity of Central FloridaOrlandoUSA
  2. 2.Medical Image Processing Group (MIPG), Department of RadiologyUniversity of PennsylvaniaPhiladelphiaUSA
  3. 3.Computational Radiology Laboratory (CRL), Department of RadiologyBoston Children’s Hospital and Harvard Medical SchoolBostonUSA

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