Abstract
This paper presents an algorithm for fast segmentation of white matter bundles from massive dMRI tractography datasets using a multisubject atlas. We use a distance metric to compare streamlines in a subject dataset to labeled centroids in the atlas, and label them using a per-bundle configurable threshold. In order to reduce segmentation time, the algorithm first preprocesses the data using a simplified distance metric to rapidly discard candidate streamlines in multiple stages, while guaranteeing that no false negatives are produced. The smaller set of remaining streamlines is then segmented using the original metric, thus eliminating any false positives from the preprocessing stage. As a result, a single-thread implementation of the algorithm can segment a dataset of almost 9 million streamlines in less than 6 minutes. Moreover, parallel versions of our algorithm for multicore processors and graphics processing units further reduce the segmentation time to less than 22 seconds and to 5 seconds, respectively. This performance enables the use of the algorithm in truly interactive applications for visualization, analysis, and segmentation of large white matter tractography datasets.
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References
Beaulieu, C. (2002). The basis of anisotropic water diffusion in the nervous system - a technical review. NMR in Biomedicine, 15, 435–455.
Bénézit, A., Hertz-Pannier, L., Dehaene-Lambertz, G., Monzalvo, K., Germanaud, D., Duclap, D., Guevara, P., Mangin, J.F., Poupon, C., Moutard, M.L., & Dubois, J. (2015). Organising white matter in a brain without corpus callosum fibres. Cortex, 63, 155–171.
Catani, M., Howard, R.J., Pajevic, S., & Jones, D.K. (2002). Virtual in vivo interactive dissection of white matter fasciculi in the human brain. NeuroImage, 17(1), 77–94.
Catani, M., Jones, D.K., & ffytche, D.H. (2005). Perisylvian language networks of the human brain. Annals of Neurology, 57(1), 8–16.
Descoteaux, M., Angelino, E., Fitzgibbons, S., & Deriche, R. (2007). Regularized, fast and robust analytical q-ball imaging. Magnetic Resonance in Medicine, 58, 497–510.
Descoteaux, M., Deriche, R., Knösche, T.R., & Anwander, A. (2009). Deterministic and probabilistic tractography based on complex fibre orientation distributions. IEEE Transactions in Medical Imaging, 28(2), 269–286.
Dubois, J., Kulikova, S., Hertz-Pannier, L., Mangin, J.F., Dehaene-Lambertz, G., & Poupon, C. (2014). Correction strategy for diffusion-weighted images corrupted with motion: application to the dti evaluation of infants’ white matter. Magnetic Resonance Imaging, 32(8), 981–992.
Duclap, D., Lebois, A., Schmitt, B., Riff, O., Guevara, P., Marrakchi-Kacem, L., Brion, V., Poupon, F., Mangin, J. F., & Poupon, C. (2012). Connectomist-2.0: a novel diffusion analysis toolbox for BrainVISA. In ESMRMB 2012.
Eklund, A., Dufort, P., Forsberg, D., & LaConte, S.M. (2013). Medical image processing on the GPU - past, present and future. Medical Image Analysis, 17(8), 1073–1094.
Garyfallidis, E., Brett, M., Correia, M.M., Williams, G.B., & Nimmo-Smith, I. (2012). Quickbundles, a method for tractography simplification. Frontiers in Neuroscience, 6(175).
Garyfallidis, E., Ocegueda, O., Wassermann, D., & Descoteaux, M. (2015). Robust and efficient linear registration of white-matter fascicles in the space of streamlines. NeuroImage, 117, 124–140.
Golby, A.J., Kindlmann, G., Norton, I., Yarmarkovich, A., Pieper, S., & Kikinis, R. (2011). Interactive diffusion tensor tractography visualization for neurosurgical planning. Neurosurgery, 68(2), 496–505.
Guevara, P., Poupon, C., Riviére, D., Cointepas, Y., Descoteaux, M., Thirion, B., & Mangin, J.F. (2011). Robust clustering of massive tractography datasets. NeuroImage, 54(3), 1975–1993.
Guevara, P., Duclap, D., Poupon, C., Marrakchi-Kacem, L., Fillard, P., Lebihan, D., Leboyer, M., Houenou, J., & Mangin, J.F. (2012). Automatic fiber bundle segmentation in massive tractography datasets using a multi-subject bundle atlas. NeuroImage, 61(4), 1083–1099.
Lawes, I.N.C., Barrick, T.R., Murugam, V., Spierings, N., Evans, D.R., Song, M., & Clark, C.A. (2008). Atlas-based segmentation of white matter tracts of the human brain using diffusion tensor tractography and comparison with classical dissection. NeuroImage, 39(1), 62–79.
Le Bihan, D., Mangin, J.F., Poupon, C., Clark, C.A., Pappata, S., Molko, N., & Chabriat, H. (2001). Diffusion tensor imaging: concepts and applications. Journal of Magnetic Resonance Imaging, 13(4), 534–546.
Lee, J., & Kim, D.S. (2013). Divide et impera: Acceleration of dti tractography using multi-gpu parallel processing. 2013 Wiley Periodicals. Inc Int J Imaging Published online in Wiley Online Library, 23(30), 256–264.
Maddah, M., Eric, W., Grimson, L., Warfield, S.K., & Wells, W.M. (2007). A unified framework for clustering and quantitative analysis of white matter fiber tracts. Medical Image Analysis, 12(2), 191–202.
Mai, S.T., Goebl, S., & Plant, C. (2012). A similarity model and segmentation algorithm for white matter fiber tracts. In IEEE 12th International Conference on Data Mining (pp. 1014–1019).
Mittmann, A., Nobrega, T.H.C., Comunello, E., Pinto, J.P.O., Dellani, P.R., Stoeter, P., & von Wangenheim, A. (2011). Performing real-time interactive fiber tracking. Journal of Digital Imaging, 24(2), 339–351.
Nowinski, W.L., Chua, B.C., Yang, G.L., & Qian, G.Y. (2011). Three-dimensional interactive and stereotactic human brain atlas of white matter tracts. Neuroinformatics, 10(1), 33–55.
O’Donnell, L., & Westin, C.F. (2007). Automatic tractography segmentation using a high-dimensional white matter atlas. IEEE Transactions on Medical Imaging, 26(11), 1562–1575.
O’Donnell, L.J., Westin, C.F., & Golby, A.J. (2009). Tract-based morphometry for white matter group analysis. NeuroImage, 45(3), 832–844.
Prados, F., Boada, I., Feixas, M., Prats-Galino, A., Blasco, G., Puig, J., & Pedraza, S. (2012). Information-theoretic approach for automated white matter fiber tracts reconstruction. Neuroinformatics, 10(3), 305–318.
Ros, C., Tandetzky, R., Güllmar, D., & Reichenbach, J. (2011). GPGPU computing for the cluster analysis of fiber tracts: Replacing a $ 15000 high end PC with a $500 graphics card. In ISMRM 2011.
Ros, C., Güllmar, D., Stenze, M., Mentzel, H.J., & Reichenbach, J.R. (2012). Quantitative fiber bundle-based analysis of diffusion-weighted mri data. Biomed Tech (Berl), 57(SI-1), 530–533.
Ros, C., Gllmar, D., Stenzel, M., Mentzel, H.J., & Reichenbach, J.R. (2013). Atlas-guided cluster analysis of large tractography datasets. PLoS One 8(12), e83, 847.
Sarrazin, S., Poupon, C., Linke, J., Wessa, M., Phillips, M., Delavest, M., Versace, A., Almeida, J., Guevara, P., Duclap, D., Duchesnay, E., Mangin, J.F., Dudal, K.L., Daban, C., Hamdani, N., D’Albis, M.A., Leboyer, M., & Houenou, J. (2014). A multicenter tractography study of deep white matter tracts in bipolar i disorder: Psychotic features and interhemispheric disconnectivity. JAMA Psychiatry, 71(4), 388–396.
Schmitt, B., Lebois, A., Duclap, D., Guevara, P., Poupon, F., Rivière, D., Cointepas, Y., LeBihan, D., Mangin, J.F., & Poupon, C. (2012). Connect/archi: an open database to infer atlases of the human brain connectivity. In ESMRMB conference.
Sullivan, E.V., Rohlfing, T., & Pfefferbaum, A. (2010). Quantitative fiber tracking of lateral and interhemispheric white matter systems in normal aging: relations to timed performance. Neurobiology of Aging, 31(3), 464–481.
Sun, Z.Y., Houenou, J., Duclap, D, Sarrazin, S., Linke, J., Daban, C., Hamdani, N., d’Albis, M.A., Corvoisier, P.L., Guevara, P., Delavest, M., Bellivier, F., Almeida, J., Versace, A., Poupon, C., Leboyer, M., Phillips, M., Wessa, M., & Mangin, J.F. (2016). Shape analysis of the cingulum, uncinate and arcuate fasciculi in patients with bipolar disorder. Journal of Psychiatry and Neuroscience In press.
Tuch, D.S. (2004). Q-ball imaging. Magn Reson Med, 52(6), 1358–1372.
Visser, E., Nijhuis, E.H.J., Buitelaar, J.K., & Zwiers, M.P. (2011). Partition-based mass clustering of tractography streamlines. NeuroImage, 54(1), 303–312.
Wakana, S., Caprihan, A., Panzenboeck, M.M., Fallon, J.H., Perry, M., Gollub, R.L., Hua, K., Zhang, J., Jiang, H., Dubey, P., Blitz, A., van Zijl, P., & Mori, S. (2007). Reproducibility of quantitative tractography methods applied to cerebral white matter. NeuroImage, 36(3), 630–644.
Wang, X., Grimson, W.E.L., & Westin, C.F. (2011). Tractography segmentation using a hierarchical dirichlet processes mixture model. NeuroImage, 54(1), 290–302.
Wang, Y., Du, H., Xia, M., Ren, L., & Xu, M. (2013). A hybrid cpu-gpu accelerated framework for fast mapping of high-resolution human brain connectome. PLOS ONE 8(5), e62, 789.
Wassermann, D., Bloy, L., Kanterakis, E., Verma, R., & Deriche, R. (2010). Unsupervised white matter fiber clustering and tract probability map generation: Applications of a gaussian process framework for white matter fibers. NeuroImage, 51, 228–241.
Xu, M., Zhang, X., Wang, Y., Ren, L., Wen, Z., Xu, Y., Gong, G., Xu, N., & Yang, H., (2012). Probabilistic brain fiber tractography on gpus, USA, Washington, DC.
Yoo, S.W., Guevara, P., Jeong, Y., Yoo, K., Shin, J.S., Mangin, J.F., & Seong, J.K. (2015). An example-based multi-atlas approach to automatic labeling of white matter tracts. PLoS ONE 10(7), e0133, 337.
Zhang, Y., Zhang, J., Oishi, K., & et al. (2010). Atlas-guided tract reconstruction for automated and comprehensive examination of the white matter anatomy. NeuroImage, 52(4), 1289–1301.
Acknowledgments
This work was partially funded by FONDECYT grants 1151278 and 11121644, and PIA-CONICYT PFB0824. Thanks to Marion Leboyer for providing a testing HARDI database.
The “CONNECT/ARCHI Database” is the property of the CEA I2BM NeuroSpin centre and was designed under the supervision of Dr Cyril Poupon and Dr Jean-François Mangin, and was funded by the Federative Research Institute 49, by the HIPPIP European grant, and the European CONNECT project (http://www.brain-connect.eu). Acquisitions were performed by the scientists involved in the Multi-scale Brain Architecture research program of NeuroSpin and by the staff of the UNIACT Laboratory of NeuroSpin (headed by Dr. Lucie Hertz-Pannier), under the ethical approval CPP100002/CPP100022 (principal investigator Dr. Denis Le Bihan).
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Labra, N., Guevara, P., Duclap, D. et al. Fast Automatic Segmentation of White Matter Streamlines Based on a Multi-Subject Bundle Atlas. Neuroinform 15, 71–86 (2017). https://doi.org/10.1007/s12021-016-9316-7
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DOI: https://doi.org/10.1007/s12021-016-9316-7