Abstract
Segmentation of structural and diffusion MRI (sMRI/dMRI) is usually performed independently in neuroimaging pipelines. However, some brain structures (e.g., globus pallidus, thalamus and its nuclei) can be extracted more accurately by fusing the two modalities. Following the framework of Bayesian segmentation with probabilistic atlases and unsupervised appearance modeling, we present here a novel algorithm to jointly segment multi-modal sMRI/dMRI data. We propose a hierarchical likelihood term for the dMRI defined on the unit ball, which combines the Beta and Dimroth-Scheidegger-Watson distributions to model the data at each voxel. This term is integrated with a mixture of Gaussians for the sMRI data, such that the resulting joint unsupervised likelihood enables the analysis of multi-modal scans acquired with any type of MRI contrast, b-values, or number of directions, which enables wide applicability. We also propose an inference algorithm to estimate the maximum-a-posteriori model parameters from input images, and to compute the most likely segmentation. Using a recently published atlas derived from histology, we apply our method to thalamic nuclei segmentation on two datasets: HCP (state of the art) and ADNI (legacy) – producing lower sample sizes than Bayesian segmentation with sMRI alone.
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Notes
- 1.
Henceforth, we use “Bayesian segmentation” to refer to this specific family of Bayesian methods, using probabilistic atlases and unsupervised appearance modeling.
References
Ashburner, J., Friston, K.: Unified segmentation. Neuroimage 26, 839–851 (2005)
Fischl, B., Salat, D.H., Busa, E., Albert, M., Dieterich, M., et al.: Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain. Neuron 33(3), 341–355 (2002)
Iglesias, J.E., Sabuncu, M.R.: Multi-atlas segmentation of biomedical images: a survey. Med. Im. Anal. 24(1), 205–219 (2015)
Roy, A.G., Conjeti, S., Navab, N., Wachinger, C.: QuickNAT: segmenting MRI neuroanatomy in 20 seconds. NeuroImage 186(1), 713–727 (2019)
Patenaude, B., Smith, S., Kennedy, D., Jenkinson, M.: A bayesian model of shape and appearance for subcortical brain segmentation. NeuroImage 56, 907–922 (2011)
Van Essen, D.C., Smith, S.M., Barch, D.M., Behrens, T.E., Yacoub, E., et al.: The WU-Minn human connectome project: an overview. NeuroImage 80, 62–79 (2013)
O’Donnell, L.J., Westin, C.F.: Automatic tractography segmentation using a high-dimensional white matter atlas. IEEE Trans. Med. Im. 26(11), 1562–1575 (2007)
Wassermann, D., Bloy, L., Kanterakis, E., Verma, R., Deriche, R.: Unsupervised white matter fiber clustering and tract probability map generation: applications of a Gaussian process framework for white matter fibers. NeuroImage 51, 228 (2010)
Yendiki, A., Panneck, P., Srinivasan, P., Stevens, A., Zöllei, L., et al.: Automated probabilistic reconstruction of white-matter pathways in health and disease using an atlas of the underlying anatomy. Front. Neuroinform. 5, 23 (2011)
Awate, S.P., Zhang, H., Gee, J.C.: A fuzzy, nonparametric segmentation framework for DTI and MRI analysis: with applications to DTI-tract extraction. IEEE Trans. Med. Im. 26(11), 1525–1536 (2007)
Hagler, D.J., Ahmadi, M.E., Kuperman, J., Holland, D., McDonald, C.R., et al.: Automated white-matter tractography using a probabilistic diffusion tensor atlas: application to temporal lobe epilepsy. Hum. Brain Map. 30(5), 1535–1547 (2009)
Behrens, T.E., Johansen-Berg, H., Woolrich, M., Smith, S., Wheeler-Kingshott, C., et al.: Non-invasive mapping of connections between human thalamus and cortex using diffusion imaging. Nat. Neurosci. 6(7), 750 (2003)
Saygin, Z.M., Osher, D.E., Augustinack, J., Fischl, B., Gabrieli, J.D.: Connectivity-based segmentation of human amygdala nuclei using probabilistic tractography. Neuroimage 56(3), 1353–1361 (2011)
Stough, J.V., Glaister, J., Ye, C., Ying, S.H., Prince, J.L., Carass, A.: Automatic method for thalamus parcellation using multi-modal feature classification. In: Golland, P., Hata, N., Barillot, C., Hornegger, J., Howe, R. (eds.) MICCAI 2014. LNCS, vol. 8675, pp. 169–176. Springer, Cham (2014)
Glaister, J., Carass, A., Stough, J.V., Calabresi, P.A., Prince, J.L.: Thalamus parcellation using multi-modal feature classification and thalamic nuclei priors. Proc SPIE Int. Soc. Opt. Eng. 9784, 97843J (2016)
Zhang, Y., Brady, M., Smith, S.: Segmentation of brain MR images through a hidden markov random field model and the expectation-maximization algorithm. IEEE Trans. Med. Im. 20(1), 45–57 (2001)
Iglesias, J.E., Insausti, R., Lerma-Usabiaga, G., Bocchetta, M., Van Leemput, K., et al.: A probabilistic atlas of the human thalamic nuclei combining ex vivo MRI and histology. NeuroImage 183, 314–326 (2018)
Van Leemput, K.: Encoding probabilistic brain atlases using bayesian inference. IEEE Trans. Med. Im. 28(6), 822 (2009)
Zhang, H., Schneider, T., Wheeler-Kingshott, C.A., Alexander, D.C.: NODDI: practical in vivo neurite orientation dispersion and density imaging of the human brain. Neuroimage 61(4), 1000–1016 (2012)
Mardia, K., Jupp, P.: Directional Statistics, vol. 494. Wiley, New York (2009)
Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum likelihood from incomplete data via the EM algorithm. J. R. Stat. Soc. B 39, 1–38 (1977)
Sotiropoulos, S.N., Jbabdi, S., Xu, J., Andersson, J.L., Moeller, S., et al.: Advances in diffusion MRI acquisition and processing in the human connectome project. Neuroimage 80, 125–143 (2013)
Arsigny, V., Fillard, P., Pennec, X., Ayache, N.: Log-euclidean metrics for fast and simple calculus on diffusion tensors. Magn. Reson. Med. 56(2), 411–421 (2006)
Yeh, F.C., Wedeen, V.J., Tseng, W.Y.I.: Generalized q-sampling imaging. IEEE Trans. Med. Im. 29(9), 1626–1635 (2010)
Acknowledgement
Supported by Horizon 2020 (ERC Starting Grant 677697, Marie Curie grant 765148), Danish Council for Independent Research (DFF-6111-00291), NIH (R21AG050122, P41EB015902), Wistron Corp., SIP, and AWS.
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Iglesias, J.E., Van Leemput, K., Golland, P., Yendiki, A. (2019). Joint Inference on Structural and Diffusion MRI for Sequence-Adaptive Bayesian Segmentation of Thalamic Nuclei with Probabilistic Atlases. In: Chung, A., Gee, J., Yushkevich, P., Bao, S. (eds) Information Processing in Medical Imaging. IPMI 2019. Lecture Notes in Computer Science(), vol 11492. Springer, Cham. https://doi.org/10.1007/978-3-030-20351-1_60
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