Advertisement

Automatic Accurate Infant Cerebellar Tissue Segmentation with Densely Connected Convolutional Network

  • Jiawei Chen
  • Han Zhang
  • Dong Nie
  • Li Wang
  • Gang Li
  • Weili Lin
  • Dinggang Shen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11046)

Abstract

The human cerebellum has been recognized as a key brain structure for motor control and cognitive function regulation. Investigation of brain functional development in the early life has recently been focusing on both cerebral and cerebellar development. Accurate segmentation of the infant cerebellum into different tissues is among the most important steps for quantitative development studies. However, this is extremely challenging due to the weak tissue contrast, extremely folded structures, and severe partial volume effect. To date, there are very few works touching infant cerebellum segmentation. We tackle this challenge by proposing a densely connected convolutional network to learn robust feature representations of different cerebellar tissues towards automatic and accurate segmentation. Specifically, we develop a novel deep neural network architecture by directly connecting all the layers to ensure maximum information flow even among distant layers in the network. This is distinct from all previous studies. Importantly, the outputs from all previous layers are passed to all subsequent layers as contextual features that can guide the segmentation. Our method achieved superior performance than other state-of-the-art methods when applied to Baby Connectome Project (BCP) data consisting of both 6- and 12-month-old infant brain images.

Notes

Acknowledgements

This work was supported by the National Institutes of Health (MH109773, MH100217, MH070890, EB006733, EB008374, EB009634, AG041721, AG042599, MH088520, MH108914, and MH107815). This work also utilizes approaches developed by an NIH grant (1U01MH110274) and the efforts of the UNC/UMN Baby Connectome Project Consortium.

References

  1. 1.
    Li, G., Lin, W., Gilmore, J.H., Shen, D.: Spatial patterns, longitudinal development, and hemispheric asymmetries of cortical thickness in infants from birth to 2 years of age. J. Neurosci. 35(24), 9150–9162 (2015)CrossRefGoogle Scholar
  2. 2.
    Wolf, U., Rapoport, M.J., Schweizer, T.A.: Evaluating the affective component of the cerebellar cognitive affective syndrome. J. Neuropsychiatry Clin. Neurosci. 21(3), 245–253 (2009)CrossRefGoogle Scholar
  3. 3.
    Poretti, A., Boltshauser, E., Huisman, T.A.: Pre-and postnatal neuroimaging of congenital cerebellar abnormalities. The Cerebellum 15(1), 5–9 (2016)CrossRefGoogle Scholar
  4. 4.
    Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431–3440 (2015)Google Scholar
  5. 5.
    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_28CrossRefGoogle Scholar
  6. 6.
    Milletari, F., Navab, N., Ahmadi, S.A.: V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 Fourth International Conference on 3D Vision (3DV), pp. 565–571. IEEE (2016)Google Scholar
  7. 7.
    Çiçek, Ö., Abdulkadir, A., Lienkamp, S.S., Brox, T., Ronneberger, O.: 3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, Gozde, Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 424–432. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-46723-8_49CrossRefGoogle Scholar
  8. 8.
    Chen, H., Dou, Q., Yu, L., Qin, J., Heng, P.A.: VoxResNet: Deep voxelwise residual networks for brain segmentation from 3D MR images. NeuroImage (2017)Google Scholar
  9. 9.
    Huang, G., et al.: Densely connected convolutional networks. arXiv preprint arXiv:1608.06993 (2016)
  10. 10.
    Yu, L., Cheng, J.-Z., Dou, Q., Yang, X., Chen, H., Qin, J., Heng, P.-A.: Automatic 3D Cardiovascular MR Segmentation with Densely-Connected Volumetric ConvNets. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10434, pp. 287–295. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-66185-8_33CrossRefGoogle Scholar
  11. 11.
    Wang, L., Gao, Y., Shi, F., Li, G., Gilmore, J.H., Lin, W., Shen, D.: LINKS: learning-based multi-source Integration frameworK for segmentation of infant brain images. Neuroimage 108, 160–172 (2015)CrossRefGoogle Scholar
  12. 12.
    Jia, Y., et al.: Caffe: Convolutional architecture for fast feature embedding. In: Proceedings of the 22nd ACM International Conference on Multimedia, pp. 675–678 (2014)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Jiawei Chen
    • 1
  • Han Zhang
    • 1
  • Dong Nie
    • 1
  • Li Wang
    • 1
  • Gang Li
    • 1
  • Weili Lin
    • 1
  • Dinggang Shen
    • 1
  1. 1.Department of Radiology and BRICUniversity of North Carolina at Chapel HillChapel HillUSA

Personalised recommendations