Abstract
Segmentation of whole-brain fiber tractography into anatomically meaningful fiber bundles is an important step for visualizing and quantitatively assessing white matter tracts. The fiber streamlines in whole-brain fiber tractography are 3D curves, and they are densely and complexly connected throughout the brain. Due to the huge volume of curves, varied connection complexity, and imaging technology limitations, whole-brain tractography segmentation is still a difficult task. In this study, a novel deep learning architecture has been proposed for segmenting whole-brain tractography into 10 major white matter bundles and the “other fibers” category. The proposed PointNet based CNN architecture takes the whole-brain fiber curves in the form of 3D raw curve points and successfully segments them using a manually created large-scale training dataset. To improve segmentation performance, the approach employs two channel-spatial attention modules. The proposed method was tested on healthy adults across the lifespan with imaging data from the ADNI project database. In terms of experimental evidence, the proposed deep learning architecture demonstrated solid performance that is better than the state-of-the-art.
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Catani, M., De Schotten, M.T.J.C.: A diffusion tensor imaging tractography atlas for virtual in vivo dissections. Cortex 44, 1105–1132 (2008)
Wakana, S., et al.: Reproducibility of quantitative tractography methods applied to cerebral white matter. Neuroimage 36, 630–644 (2007)
Hua, K., Zhang, J., Wakana, S., Jiang, H., Li, X., Reich, D.S., et al.: Tract probability maps in stereotaxic spaces: analyses of white matter anatomy and tract-specific quantification. Neuroimage 39, 336–347 (2008)
Wassermann, D., et al.: The white matter query language: a novel approach for describing human white matter anatomy. Brain Struct. Funct. 221(9), 4705–4721 (2016). https://doi.org/10.1007/s00429-015-1179-4
Zhang, S., Correia, S., Laidlaw, D.: Identifying white-matter fiber bundles in DTI data using an automated proximity-based fiber clustering method. IEEE Trans. Vis. Comput. Graph. 14, 1044 (2008)
Ratnarajah, N., Simmons, A., Hojjatoleslami, A.: Probabilistic clustering and shape modelling of white matter fibre bundles using regression mixtures. In: Fichtinger, G., Martel, A., Peters, T. (eds.) MICCAI 2011. LNCS, vol. 6892, pp. 25–32. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-23629-7_4
Ye, C., Yang, Z., Ying, S.H., Prince, J.L.J.N.: Segmentation of the cerebellar peduncles using a random forest classifier and a multi-object geometric deformable model: application to spinocerebellar ataxia type 6. Neuroinformatics 13, 367–381 (2015)
Ratnarajah, N., Qiu, A.J.N.: Multi-label segmentation of white matter structures: application to neonatal brains. NeuroImage 102, 913–922 (2014)
Wasserthal, J., Neher, P., Maier-Hein, K.H.J.N.: TractSeg-fast and accurate white matter tract segmentation. NeuroImage 183, 239–253 (2018)
Reisert, M., Coenen, V.A., Kaller, C., Egger, K., Skibbe, H.J.: HAMLET: hierarchical harmonic filters for learning tracts from diffusion MRI. arXiv preprint arXiv:01068 (2018)
Li, B., et al.: Neuro4Neuro: a neural network approach for neural tract segmentation using large-scale population-based diffusion imaging. NeuroImage 218, 116993 (2020)
Zhang, F., Karayumak, S.C., Hoffmann, N., Rathi, Y., Golby, A.J., O’Donnell, L.J.J.M.I.A.: Deep white matter analysis (DeepWMA): fast and consistent tractography segmentation. Med. Image Anal. 65, 101761 (2020)
Liu, F., et al.: DeepBundle: fiber bundle parcellation with graph convolution neural networks. In: International Workshop on Graph Learning in Medical Imaging, pp. 88–95 (2019)
Gupta, V., Thomopoulos, S.I., Corbin, C.K., Rashid, F., Thompson, P.M.: Fibernet 2.0: an automatic neural network based tool for clustering white matter fibers in the brain. In: 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), pp. 708–711 (2018)
Gupta, V., Thomopoulos, S.I., Rashid, F.M., Thompson, P.M.: FiberNET: an ensemble deep learning framework for clustering white matter fibers. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 548–555 (2017)
“ADNI”: Usc.edu. http://adni.loni.usc.edu/. Accessed 27 Jan 2021
Smith, S.M., Jenkinson, M., Woolrich, M.W., Beckmann, C.F., Behrens, T.E., JohansenBerg, H., et al.: Advances in functional and structural MR image analysis and implementation as FSL. Neuroimage 23(Suppl 1), S208-19 (2004)
Jenkinson, M., Bannister, P., Brady, M., Smith, S.: Improved optimization for the robust and accurate linear registration and motion correction of brain images. Neuroimage 17, 825–841 (2002)
Qi, C.R., Su, H., Mo, K., Guibas, L.J.,: Pointnet: deep learning on point sets for 3D classification and segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 652–660 (2017)
Woo, S., Park, J., Lee, J., Kweon, I.S.: CBAM: convolutional block attention module. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 3–19 (2018)
Gupta, T., Patil, S.M., Tailor, M., Thapar, D., Nigam, A.J.: BrainSegNet: a segmentation network for human brain fiber tractography data into anatomically meaningful clusters. arXiv preprint arXiv:05158 (2017)
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, pp. 1–6 (2016)
Patil, S.M., Nigam, A., Bhavsar, A., Chattopadhyay, C.J.,: Siamese LSTM based fiber structural similarity network (FS2Net) for rotation invariant brain tractography segmentation. arXiv preprint arXiv:09792 (2017)
Lu, Q., Li, Y., Ye, C.J.M.I.A.: Volumetric white matter tract segmentation with nested self-supervised learning using sequential pretext tasks. Med. Image Anal. 72, 102094 (2021)
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Kumaralingam, L., Thanikasalam, K., Sotheeswaran, S., Mahadevan, J., Ratnarajah, N. (2022). Segmentation of Whole-Brain Tractography: A Deep Learning Algorithm Based on 3D Raw Curve Points. 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_18
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