Abstract
Fiber tract segmentation is a prerequisite for the tract-based statistical analysis and plays a crucial role in understanding brain structure and function. The previous researches mainly consist of two steps: defining and computing the similarity features of fibers, and then adopting machine learning algorithm for clustering or classification. Among them, how to define similarity is the basic premise and assumption of the whole method, and determines its potential reliability and application. The similarity features defined by previous studies ranged from geometric to anatomical, and then to functional characteristics, accordingly, the resulting fiber tracts seem more and more meaningful, while their reliability declined. Therefore, here we still adopt geometric feature for fiber tract segmentation, and put forward a novel descriptor (FiberGeoMap) for representing fiber’s geometric feature, which can depict effectively the shape and position of fiber, and can be inputted into our revised Transformer encoder network, called as FiberGeoMap Learner, which can well fully leverage the fiber’s features. Experimental results showed that the FiberGeoMap combined with FiberGeoMap Learner can effectively express fiber’s geometric features, and differentiate the 103 various fiber tracts, furthermore, the common fiber tracts across individuals can be identified by this method, thus avoiding additional image registration in preprocessing. The comparative experiments demonstrated that the proposed method had better performance than the existing methods. The code and more details are openly available at https://github.com/Garand0o0/FiberTractSegmentation.
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
Mandl, R.C., et al.: Altered white matter connectivity in never-medicated patients with schizophrenia. Hum. Brain Mapp. 34(9), 2353–2365 (2013)
Propper, R.E., et al.: A combined fMRI and DTI examination of functional language lateralization and arcuate fasciculus structure: effects of degree versus direction of hand preference. Brain Cogn. 73(2), 85–92 (2010)
Zhang, F., Cetin Karayumak, S., Hoffmann, N., Rathi, Y., Golby, A.J., O’Donnell, L.J.: Deep white matter analysis (DeepWMA): fast and consistent tractography segmentation. Med. Image Anal. 65, 101761 (2020)
Prince, D., Lam, N., Gaetan, et al.: TRAFIC: fiber tract classification using deep learning. In: Proceedings of SPIE–The International Society for Optical Engineering, vol. 10574, p. 1057412(2018)
Gerig, G., Gouttard, S., Corouge, I.: Analysis of brain white matter via fiber tract modeling. In: The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4421–4424 (2004)
Tunç, B., et al.: Multinomial probabilistic fiber representation for connectivity driven clustering. In: Gee, J.C., Joshi, S., Pohl, K.M., Wells, W.M., Zöllei, L. (eds.) IPMI 2013. LNCS, vol. 7917, pp. 730–741. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-38868-2_61
Wassermann, D., Nikos, M., Rathi, Y., et al.: The white matter query language: a novel approach for describing human white matter anatomy. Brain Struct. Funct. 221(9), 1–17 (2016)
Ge, B., Guo, L., Zhang, T., Hu, X., Han, J., Liu, T.: Resting state fMRI-guided fiber clustering: methods and applications. Neuroinformatics 11(1), 119–133 (2013)
Wang, H., Qiang, N., Ge, B., Liu, T.: Task fMRI guided fiber clustering via a deep clustering method. In: IEEE 17th International Symposium on Biomedical Imaging (ISBI), Iowa City, IA, USA (2020)
Elliott, M., et al.: What is the test-retest reliability of common task-fMRI measures? New empirical evidence and a meta-analysis. Biol. Psychiatry 87(9), S132–S133 (2020)
Wasserthal, J., Neher, P., et al.: TractSeg-fast and accurate while matter tract segmentation. Neuroimage 183, 239–253 (2018)
Essen, D., Smith, S.M., Barch, D.M., Behrens, T., Ugurbil, K.: The WU-Minn Human Connectome Project: an overview. Neuroimage 80, 62–79 (2013)
Vaswani, A., Shazeer, N., Parmar, N., et al.: Attention is all you need. arXiv (2017)
Taha, A.A., Hanbury, A.: Metrics for evaluating 3D medical image segmentation: analysis, selection, and tool. BMC Med. Imaging 15, 29 (2015)
O’Donnell, L.J., Suter, Y., Rathi, Y., et al.: Automated white matter fiber tract identification in patients with brain tumors. Neuroimage 13, 138–153 (2016)
Dimond, D., Schuetze, M., Smith, R.E., et al.: Reduced white matter fiber density in autism spectrum disorder. Cereb Cortex. 29(4), 1778–1788 (2019)
Zhang, F., et al.: An anatomically curated fiber clustering white matter atlas for consistent white matter tract parcellation across the lifespan. Neuroimage 179, 429–447 (2018)
Acknowledgement
The work was supported by the National Natural Science Foundation of China (NSFC61976131 and NSFC61936007).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
1 Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Wang, Z., Lv, Y., He, M., Ge, E., Qiang, N., Ge, B. (2022). Accurate Corresponding Fiber Tract Segmentation via FiberGeoMap Learner. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2022. MICCAI 2022. Lecture Notes in Computer Science, vol 13431. Springer, Cham. https://doi.org/10.1007/978-3-031-16431-6_14
Download citation
DOI: https://doi.org/10.1007/978-3-031-16431-6_14
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-16430-9
Online ISBN: 978-3-031-16431-6
eBook Packages: Computer ScienceComputer Science (R0)
