Abstract
In this paper, we propose a novel deep learning architecture combining stacked Bi-directional LSTM and LSTMs with the Siamese network architecture for segmentation of brain fibers, obtained from tractography data, into anatomically meaningful clusters. The proposed network learns the structural difference between fibers of different classes, which enables it to classify fibers with high accuracy. Importantly, capturing such deep inter and intra class structural relationship also ensures that the segmentation is robust to relative rotation among test and training data, hence can be used with unregistered data. Our extensive experimentation over order of hundred-thousands of fibers shows that the proposed model achieves state-of-the-art results, even in cases of large relative rotations between test and training data.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Catani, M., Howard, R.J., Pajevic, S., Jones, D.K.: Virtual in vivo interactive dissection of white matter fasciculi in the human brain. Neuroimage 17(1), 77–94 (2002)
Gupta, T., Patil, S.M., Tailor, M., Thapar, D., Nigam, A.: BrainSegNet: a segmentation network for human brain fiber tractography data into anatomically meaningful clusters. arXiv:1710.05158 (2017)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
Koch, G., Zemel, R., Salakhutdinov, R.: Siamese neural networks for one-shot image recognition. In: ICML Deep Learning Workshop, vol. 2 (2015)
Maddah, M., Mewes, A., Haker, S., Grimson, W., Warfield, S.: Automated atlas-based clustering of white matter fiber tracts from DTMRI. In: Medical Image Computing And Computer-Assisted Intervention–MICCAI 2005, pp. 188–195 (2005)
Nikulin, V., McLachlan, G.J.: Identifying fiber bundles with regularised \(\upkappa \)-means clustering applied to the grid-based data. In: The 2010 International Joint Conference on Neural Networks (IJCNN), pp. 1–8, July 2010
O’Donnell, L.J., Westin, C.-F.: Automatic tractography segmentation using a high-dimensional white matter atlas. IEEE Trans. Med. Imaging 26(11), 1562–1575 (2007)
Patel, V., Parmar, A., Bhavsar, A., Nigam, A.: Automated brain tractography segmentation using curvature points. In: Proceedings of the Tenth Indian Conference on Computer Vision, Graphics and Image Processing, p. 18. ACM (2016)
Poulin, P., et al.: Learn to track: deep learning for tractography. bioRxiv, p. 146688 (2017)
Sutskever, I.: Training recurrent neural networks. Ph.D. thesis, University of Toronto (2013)
Wang, X., Grimson, W.E.L., Westin, C.-F.: Tractography segmentation using a hierarchical dirichlet processes mixture model. NeuroImage 54(1), 290–302 (2011)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Jha, R.R., Patil, S., Nigam, A., Bhavsar, A. (2019). FS2Net: Fiber Structural Similarity Network (FS2Net) for Rotation Invariant Brain Tractography Segmentation Using Stacked LSTM Based Siamese Network. In: Vento, M., Percannella, G. (eds) Computer Analysis of Images and Patterns. CAIP 2019. Lecture Notes in Computer Science(), vol 11679. Springer, Cham. https://doi.org/10.1007/978-3-030-29891-3_40
Download citation
DOI: https://doi.org/10.1007/978-3-030-29891-3_40
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-29890-6
Online ISBN: 978-3-030-29891-3
eBook Packages: Computer ScienceComputer Science (R0)