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Deep Learning Methods for Identification of White Matter Fiber Tracts: Review of State-of-the-Art and Future Prospective

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Abstract

Quantitative analysis of white matter fiber tracts from diffusion Magnetic Resonance Imaging (dMRI) data is of great significance in health and disease. For example, analysis of fiber tracts related to anatomically meaningful fiber bundles is highly demanded in pre-surgical and treatment planning, and the surgery outcome depends on accurate segmentation of the desired tracts. Currently, this process is mainly done through time-consuming manual identification performed by neuro-anatomical experts. However, there is a broad interest in automating the pipeline such that it is fast, accurate, and easy to apply in clinical settings and also eliminates the intra-reader variabilities. Following the advancements in medical image analysis using deep learning techniques, there has been a growing interest in using these techniques for the task of tract identification as well. Recent reports on this application show that deep learning-based tract identification approaches outperform existing state-of-the-art methods. This paper presents a review of current tract identification approaches based on deep neural networks. First, we review the recent deep learning methods for tract identification. Next, we compare them with respect to their performance, training process, and network properties. Finally, we end with a critical discussion of open challenges and possible directions for future works.

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References

  • Abou Arkoub, S., et al. (2020). Survey on deep learning techniques for medical imaging application area. In Machine Learning Paradigms: Advances in Deep Learning-based Technological Applications, G. A. Tsihrintzis and L. C. Jain, Eds. Cham: Springer International Publishing, pp. 149–189.

  • Akkus, Z., Galimzianova, A., Hoogi, A., Rubin, D. L., & Erickson, B. J. (2017). Deep Learning for Brain MRI Segmentation: State of the Art and Future Directions. Journal of Digital Imaging, 30(4), 449–459.

    Article  PubMed  PubMed Central  Google Scholar 

  • Astolfi, P., et al. (2020). A stem-based dissection of inferior fronto-occipital fasciculus with a deep learning model. In IEEE 17th International Symposium on Biomedical Imaging - ISBI 2020, pp. 267–270.

  • Avital, I., Nelkenbaum, I., Tsarfaty, G., Konen, E., Kiryati, N., & Mayer, A. (2020). Neural segmentation of seeding ROIs (sROIs) for pre-surgical brain tractography. IEEE Transactions on Medical Imaging, 39(5), 1655–1667.

    Article  PubMed  Google Scholar 

  • Azizi, S., et al. (2021). Big self-supervised models advance medical image classification. In Proceedings of the IEEE/CVF International Conference on Computer Vision - CVF 2021, pp. 3478–3488.

  • Bai, W., et al. (2019). Self-supervised learning for cardiac mr image segmentation by anatomical position prediction. In Medical Image Computing and Computer Assisted Intervention - MICCAI 2019, pp. 541–549.

  • Baltatzis, V., et al. (2021). The pitfalls of sample selection: a case study on lung nodule classification. In Predictive Intelligence in Medicine, pp. 201–211.

  • Basser, P. J., Mattiello, J., & LeBihan, D. (1994). MR diffusion tensor spectroscopy and imaging. Biophysical Journal, 66(1), 259–267.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Basser, P. J., Pajevic, S., Pierpaoli, C., Duda, J., & Aldroubi, A. (2000). In vivo fiber tractography using DT-MRI data. Magnetic Resonance in Medicine, 44(4), 625–632.

    Article  CAS  PubMed  Google Scholar 

  • Bayrak, R. G., et al. (2020). TractEM: fast protocols for whole brain deterministic tractography-based white matter atlas. bioRxiv 651935.

  • Bazin, P. L., et al. (2011). Direct segmentation of the major white matter tracts in diffusion tensor images. NeuroImage, 58(2), 458–468.

    Article  PubMed  Google Scholar 

  • Bertò, G., et al. (2021). Classifyber, a robust streamline-based linear classifier for white matter bundle segmentation. NeuroImage, 224, 117402.

    Article  PubMed  Google Scholar 

  • Blyth, R., Cook, P., & Alexander, D. C. (2003). Tractography with multiple fibre directions. In 11th annual meeting of the International Society for Magnetic Resonance in Medicine - ISMRM 2003.

  • Brun, A., Knutsson, H., Park, H. J., Shenton, M. E., & Westin, C. F. (2004). Clustering fiber traces using normalized cuts. Medical Image Computing and Computer Assisted Intervention - MICCAI 2004, 3216/2004(3216), 368–375.

  • Cabeen, R. P., Toga, A. W., & Laidlaw, D. H. (2021). Tractography processing with the sparse closest point transform. Neuroinformatics, 19(2), 367–378.

    Article  PubMed  PubMed Central  Google Scholar 

  • Catani, M. (2006). Diffusion tensor magnetic resonance imaging tractography in cognitive disorders. Current Opinion in Neurology, 19(6), 599–606.

    Article  PubMed  Google Scholar 

  • Chamberland, M., Genc, S., Raven, E. P., Parker, G. D., Cunningham, A., Doherty, J., van den Bree, M., Tax, C. M. W., & Jones, D. K. (2020). Tractometry-based anomaly detection for single-subject white matter analysis. arXiv prepr: arXiv:2005.11082

  • Chen, T., Liu, S., Chang, S., Cheng, Y., Amini, L., & Wang, Z. (2020). Adversarial robustness: from self-supervised pre-training to fine-tuning. In Proceedings of the IEEE/CVF International Conference on Computer Vision and Pattern Recognition - CVPR 2020, pp. 699–708.

  • Chen, X., et al. (2021a). Recent advances and clinical applications of deep learning in medical image analysis. Medical Image Analysis, 79, 102444.

    Article  Google Scholar 

  • Chen, Y., et al. (2021b). Deep Fiber Clustering: anatomically informed unsupervised deep learning for fast and effective white matter parcellation. In Medical Image Computing and Computer Assisted Intervention - MICCAI 2021, pp. 497–507.

  • Ciccarelli, O., Catani, M., Johansen-Berg, H., Clark, C., & Thompson, A. (2008). Diffusion-based tractography in neurological disorders: Concepts, applications, and future developments. Lancet Neurology, 7(8), 715–727.

    Article  PubMed  Google Scholar 

  • Clayden, J. D., Storkey, A. J., & Bastin, M. E. (2007). A probabilistic model-based approach to consistent white matter tract segmentation. IEEE Transactions on Medical Imaging, 26(11), 1555–1561.

    Article  PubMed  Google Scholar 

  • Clayden, J. D., Storkey, A. J., Maniega, S. M., & Bastin, M. E. (2009). Reproducibility of tract segmentation between sessions using an unsupervised modelling-based approach. NeuroImage, 45(2), 377–385.

    Article  PubMed  Google Scholar 

  • Descoteaux, M., & Deriche, R. (2009). High angular resolution diffusion mri segmentation using region-based statistical surface evolution. Journal of Mathematical Imaging and Vision, 33(2), 239–252.

    Article  Google Scholar 

  • Descoteaux, M., Deriche, R., Knösche, T. R., & Anwander, A. (2009). Deterministic and probabilistic tractography based on complex fibre orientation distributions. IEEE Transactions on Medical Imaging, 28(2), 269–286.

    Article  PubMed  Google Scholar 

  • Dong, X., Yang, Z., Peng, J., & Wu, X. (2019). Multimodality white matter tract segmentation using CNN. Proceedings of the ACM Turing Celebration Conference - ACM.

  • Dumais, F., et al. (2022). FIESTA: Autoencoders for accurate fiber segmentation in tractography. arXiv Prepr: arxiv:2212.00143

  • Eckstein, I., et al. (2009). Active fibers: Matching deformable tract templates to diffusion tensor images. NeuroImage, 47, T82–T89.

    Article  PubMed  Google Scholar 

  • Essayed, W. I., Zhang, F., Unadkat, P., Cosgrove, G. R., Golby, A. J., & O’Donnell, L. J. (2017). White matter tractography for neurosurgical planning: A topography-based review of the current state of the art. NeuroImage: Clinical , 15, 659–672.

    Article  PubMed  PubMed Central  Google Scholar 

  • Fischl, B. (2012). FreeSurfer. NeuroImage, 62(2), 774–781.

    Article  PubMed  Google Scholar 

  • Garyfallidis, E., et al. (2014). Dipy, a library for the analysis of diffusion MRI data. Frontiers in Neuroinformatics, 8, 8.

    Article  PubMed  PubMed Central  Google Scholar 

  • Garyfallidis, E., et al. (2018). Recognition of white matter bundles using local and global streamline-based registration and clustering. NeuroImage, 170, 283–295.

    Article  PubMed  Google Scholar 

  • 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.

    Article  PubMed  PubMed Central  Google Scholar 

  • Guevara, P., et al. (2012). Automatic fiber bundle segmentation in massive tractography datasets using a multi-subject bundle atlas. NeuroImage, 61(4), 1083–1099.

    Article  CAS  PubMed  Google Scholar 

  • Guo, W., Chen, Y., & Zeng, Q. (2008). A geometric flow-based approach for diffusion tensor image segmentation. Philosophical Transactions. Series A, Mathematical, Physical, and Engineering Sciences, 366(1874), 2279–2292.

    PubMed  PubMed Central  Google Scholar 

  • Gupta, T., Patil, S. M., Tailor, M., Thapar, D., & Nigam, A. (2017b). “BrainSegNet: a segmentation network for human brain fiber tractography data into anatomically meaningful clusters. arXiv Prepr: arXiv:1710.05158

  • Gupta, V., Thomopoulos, S. I., Corbin, C. K., Rashid, F., & Thompson, P. M. (2018). FIBERNET 2.0: an automatic neural network based tool for clustering white matter fibers in the brain. In IEEE 15th International Symposium on Biomedical Imaging ISBI - 2018, pp. 708–711.

  • Gupta, V., Thomopoulos, S. I., Corbin, C. K., Rashid, F., & Thompson, P. M. (2017a). FiberNET: an ensemble deep learning framework for clustering white matter fibers. In Medical Image Computing and Computer Assisted Intervention - MICCAI 2017a, pp. 548–555.

  • Hagmann, P., et al. (2007). Mapping human whole-brain structural networks with diffusion MRI. PLoS One, 2(7).

  • Hofman, A., et al. (2015). The Rotterdam Study: 2016 objectives and design update. European Journal of Epidemiology, 30(8), 661–708.

    Article  PubMed  PubMed Central  Google Scholar 

  • 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 Computer Analysis of Images and Patterns - CAIP 2019, pp. 459–469.

  • Jonasson, L., Bresson, X., Hagmann, P., Cuisenaire, O., Meuli, R., & Thiran, J. P. (2005). White matter fiber tract segmentation in DT-MRI using geometric flows. Medical Image Analysis, 9(3), 223–236.

    Article  PubMed  Google Scholar 

  • Kamnitsas, K., et al. (2017). Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation. Medical Image Analysis, 36, 61–78.

    Article  PubMed  Google Scholar 

  • Labra, N., et al. (2017). Fast automatic segmentation of white matter streamlines based on a multi-subject bundle atlas. Neuroinformatics, 15(1), 71–86.

    Article  PubMed  Google Scholar 

  • Lam, P. D. N., Belhomme, G., Ferrall, J., Patterson, B., Styner, M., & Prieto, J. C. (2018). Trafic: fiber tract classification using deep learning. Proceedings of SPIE - The International Society for Optical Engineering - SPIE 2018, 10574.

  • Legarreta, J. H., et al. (2021). Filtering in tractography using autoencoders (FINTA). Medical Image Analysis, 72, 102126.

    Article  PubMed  Google Scholar 

  • Legarreta, J. H., et al. (2022). Clustering in Tractography Using Autoencoders (CINTA). Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 13722 LNCS, 125–136.

  • Legarreta, J. H., et al. (2023). Generative Sampling in Bundle Tractography using Autoencoders (GESTA),” Medical Image Analysis, 85.

  • Lenglet, C., Rousson, M., & Deriche, R. (2006). DTI segmentation by statistical surface evolution. IEEE Transactions on Medical Imaging, 25(6), 685–700.

    Article  PubMed  Google Scholar 

  • Li, B., et al. (2020). Neuro4Neuro: A neural network approach for neural tract segmentation using large-scale population-based diffusion imaging. NeuroImage, 218, 116993.

    Article  PubMed  Google Scholar 

  • Li, B., de Groot, M., Vernooij, M. W., Ikram, M. A., Niessen, W. J., & Bron, E. E. (2018). Reproducible white matter tract segmentation using 3D U-Net on a large-scale DTI dataset. In Machine Learning in Medical Imaging, 2018, 205–213.

  • Li, S., Chen, Z., Guo, W., Zeng, Q., & Feng, Y. (2021). Two parallel stages deep learning network for anterior visual pathway segmentation. In Computational Diffusion MRI, pp. 279–290.

  • Lin, Z., et al. (2019). Fast learning of fiber orientation distribution function for MR tractography using convolutional neural network. Medical Physics, 46(7), 3101–3116.

    Article  PubMed  Google Scholar 

  • Liu, W., Lu, Q., Zhuo, Z., Liu, Y., & Ye, C. (2022). One-Shot Segmentation of Novel White Matter Tracts via Extensive Data Augmentation,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 13431 LNCS, 133–142, 2022.

  • Litjens, G., et al. (2017). A survey on deep learning in medical image analysis. Medical Image Analysis, 42, 60–88.

    Article  PubMed  Google Scholar 

  • Liu, F., et al. (2019). DeepBundle: fiber bundle parcellation with graph convolution neural networks. In Graph Learning in Medical Imaging, pp. 88–95.

  • Lu, Q., & Ye, C. (2021). Knowledge Transfer for Few-Shot Segmentation of Novel White Matter Tracts. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12729 LNCS, pp. 216–227.

  • Lu, Q., Li, Y., & Ye, C. (2021). Volumetric white matter tract segmentation with nested self-supervised learning using sequential pretext tasks. Medical Image Analysis, 72, 102094.

    Article  PubMed  Google Scholar 

  • Maddah, M., Mewes, A. U., Haker, S., Grimson, W. E. L., & Warfield, S. K. (2005). Automated atlas-based clustering of white matter fiber tracts from DTMRI. Medical Image Computing and Computer Assisted Intervention - MICCAI 2005, 8(Pt 1), 188–195, 2005.

  • Maier-Hein, K. H., Neher, P. F., & Houde, J. C., et al. (2017). The challenge of mapping the human connectome based on diffusion tractography. Nature Communications, 8, 1349.

  • Malcolm, J. G., Shenton, M. E., & Rathi, Y. (2010). Filtered multitensor tractography. IEEE Transactions on Medical Imaging, 29(9), 1664–1675.

    Article  PubMed  PubMed Central  Google Scholar 

  • Mancini, M., Vos, S. B., Vakharia, V. N., O'Keeffe, A. G., Trimmel, K., Barkhof, F., Dorfer, C., Soman, S., Winston, G. P., Wu, C., Duncan, J. S., Sparks, R., & Ourselin, S. (2019). Automated fiber tract reconstruction for surgery planning: Extensive validation in language-related white matter tracts. NeuroImage: Clinical, 23, 101883.

  • Mayer, A., Zimmerman-Moreno, G., Shadmi, R., Batikoff, A., & Greenspan, H. (2011). A supervisedfFramework for the registration and segmentation of white matter fiber tracts. IEEE Transactions on Medical Imaging, 30(1), 131–145.

    Article  PubMed  Google Scholar 

  • Milletari, F., Navab, N., & Ahmadi, S. A. (2016). V-Net: fully convolutional neural networks for volumetric medical image segmentation. Proceedings of 2016 Fourth International Conference on 3D Vision (3DV), pp. 565–571, 2016.

  • Nai, Y. H., et al. (2021). Comparison of metrics for the evaluation of medical segmentations using prostate MRI dataset. Computers in Biology and Medicine, 134, 104497.

    Article  PubMed  Google Scholar 

  • Neher, P. F., Stieltjes, B., & Maier-Hein, K. H. (2018). Anchor-Constrained Plausibility (ACP): a novel concept for assessing tractography and reducing false-positives. In Medical Image Computing and Computer Assisted Intervention - MICCAI 2018, pp. 20–27.

  • Nelkenbaum, I., Tsarfaty, G., Kiryati, N., Konen, E., & Mayer, A. (2020). Automatic segmentation of white matter tracts using multiple brain MRI sequences. In IEEE 17th International Symposium on Biomedical Imaging - ISBI 2020, pp. 368–371.

  • O’Donnell, L. J., et al. (2012). Unbiased groupwise registration of white matter tractography. International Conference on Medical Image Computing and Computer-Assisted Intervention, 15(3), 123–130.

  • O’Donnell, L. J., & Westin, C. F. (2007). Automatic tractography segmentation using a high-dimensional white matter atlas. IEEE Transactions on Medical Imaging, 26(11), 1562–1575.

    Article  PubMed  Google Scholar 

  • Olivetti, E., & Avesani, P. (2011). Supervised segmentation of fiber tracts. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7005 LNCS, pp. 261–274.

  • Pomiecko, K., Sestili, C., Fissell, K., Pathak, S., Okonkwo, D., & Schneider, W. (2019). 3D convolutional neural network segmentation of white matter tract masks from MR diffusion anisotropy maps. In IEEE 16th International Symposium on Biomedical Imaging - ISBI 2019, pp. 1–5.

  • Poulin, P., Jörgens, D., Jodoin, P. M., & Descoteaux, M. (2019). Tractography and machine learning: Current state and open challenges. Magnetic Resonance Imaging, 64(January), 37–48.

    Article  PubMed  Google Scholar 

  • Poulin, P., Theaud, G., Rheault, F., et al. (2022). TractoInferno - A large-scale, open-source, multi-site database for machine learning dMRI tractography. Scientific Data, 9, 725.

    Article  PubMed  PubMed Central  Google Scholar 

  • Ratnarajah, N., & Qiu, A. (2014). Multi-label segmentation of white matter structures: Application to neonatal brains. NeuroImage, 102, 913–922.

    Article  PubMed  Google Scholar 

  • Reisert, M., Coenen, V. A., Kaller, C., Egger, K., & Skibbe, H. (2018). HAMLET: hierarchical harmonic filters for learning tracts from diffusion MRI. arXiv Prepr: arXiv:1807.01068

  • Ronneberger, O., Fischer, P., & Brox, T. (2015). U-net: convolutional networks for biomedical image segmentation. In Medical Image Computing and Computer Assisted Intervention -- MICCAI 2015, pp. 234–241.

  • Schilling, K. G., et al. (2021). Tractography dissection variability: What happens when 42 groups dissect 14 white matter bundles on the same dataset? NeuroImage, 243, 118502.

  • Sharmin, N., Olivetti, E., & Avesani, P. (2018). White matter tract segmentation as multiple linear assignment problems. Frontiers in Neuroscience, 11, 754.

  • Siless, V., Chang, K., Fischl, B., & Yendiki, A. (2018). AnatomiCuts: Hierarchical clustering of tractography streamlines based on anatomical similarity. NeuroImage, 166, 32–45.

    Article  PubMed  Google Scholar 

  • Stieltjes, B., et al. (2001). Diffusion tensor imaging and axonal tracking in the human brainstem. NeuroImage, 14(3), 723–735.

    Article  CAS  PubMed  Google Scholar 

  • Tao, X., Li, Y., Zhou, W., Ma, K., & Zheng, Y. (2020). Revisiting Rubik’s Cube: self-supervised learning with volume-wise transformation for 3D medical image segmentation. In Medical Image Computing and Computer Assisted Intervention - MICCAI 2020, pp. 238–248.

  • Theaud, G., Houde, J. C., Boré, A., Rheault, F., Morency, F., & Descoteaux, M. (2020). TractoFlow: A robust, efficient and reproducible diffusion MRI pipeline leveraging Nextflow & Singularity. NeuroImage, 218, 116889.

    Article  PubMed  Google Scholar 

  • Thiebaut de Schotten, M., et al. (2011). Atlasing location, asymmetry and inter-subject variability of white matter tracts in the human brain with MR diffusion tractography. NeuroImage, 54(1), 49–59.

    Article  PubMed  Google Scholar 

  • Tournier, J. D., Calamante, F., & Connelly, A. (2007). Robust determination of the fibre orientation distribution in diffusion MRI: Non-negativity constrained super-resolved spherical deconvolution. NeuroImage, 35(4), 1459–1472.

    Article  PubMed  Google Scholar 

  • Tournier, J. D., et al. (2019). MRtrix3: A fast, flexible and open software framework for medical image processing and visualisation. NeuroImage, 202, 116137.

    Article  PubMed  Google Scholar 

  • Tunç, B., Parker, W. A., Ingalhalikar, M., & Verma, R. (2014). Automated tract extraction via atlas based adaptive Clustering. NeuroImage, 102, 596–607.

    Article  PubMed  Google Scholar 

  • Ugurlu, D., Firat, Z., Ture, U., & Unal, G. (2019). Supervised classification of white matter fibers based on neighborhood fiber orientation distributions using an ensemble of neural networks. In Medical Image Computing and Computer Assisted Intervention - MICCAI 2019, pp. 143–154.

  • Van Essen, D. C., et al. (2014). Chapter 16 - Mapping connections in humans and non-human primates: aspirations and challenges for diffusion imaging. In Diffusion MRI (Second Edition), Second Edi., H. Johansen-Berg and T. E. J. Behrens, Eds. San Diego: Academic Press pp. 337–358.

  • Van Essen, D. C., et al. (2012). The Human Connectome Project: A data acquisition perspective. NeuroImage, 62(4), 2222–2231.

    Article  PubMed  Google Scholar 

  • Vázquez, A., et al. (2020). FFClust: Fast fiber clustering for large tractography datasets for a detailed study of brain connectivity. NeuroImage, 220, 117070.

    Article  PubMed  Google Scholar 

  • Vercruysse, D., Christiaens, D., Maes, F., Sunaert, S., & Suetens, P. (2014). Fiber bundle segmentation using spectral embedding and supervised learning. In Computational Diffusion MRI, pp. 103–114.

  • Wakana, S., et al. (2007). Reproducibility of quantitative tractography methods applied to cerebral white matter. NeuroImage, 36(3), 630–644.

    Article  PubMed  Google Scholar 

  • Wang, Z., He, M., Lv, Y., Ge, E., Zhang, S., Qiang, N., et al. (2022). Accurate Corresponding Fiber Tract Segmentation via FiberGeoMap Learner. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 13431 LNCS, pp. 143–152, 2022.

  • Wassermann, D., et al. (2016). The white matter query language: A novel approach for describing human white matter anatomy. Brain Structure & Function, 221(9), 4705–4721.

    Article  Google Scholar 

  • Wasserthal, J., Neher, P. F., Isensee, F., & Maier-Hein, K. H. (2017). Direct white matter bundle segmentation using stacked u-nets. arXiv Prepr: arXiv:1703.02036

  • Wasserthal, J., Neher, P., & Maier-Hein, K. H. (2018a). TractSeg - Fast and accurate white matter tract segmentation. NeuroImage, 183(March), 239–253.

    Article  PubMed  Google Scholar 

  • Wasserthal, J., Neher, P. F., & Maier-Hein, K. H. (2018b). Tract orientation mapping for bundle-specific tractography. In Medical Image Computing and Computer Assisted Intervention - MICCAI 2018, pp. 36–44.

  • Wasserthal, J., Neher, P. F., Hirjak, D., & Maier-Hein, K. H. (2019). Combined tract segmentation and orientation mapping for bundle-specific tractography. Medical Image Analysis, 58, 101559.

    Article  PubMed  Google Scholar 

  • Xu, C., Sun, G., Liang, R., & Xu, X. (2020). Vector field streamline clustering framework for brain fiber tract segmentation. arXiv e-prints: arXiv:2011.01795

  • Xu, H., Dong, M., Lee, M. H., O’Hara, N., Asano, E., & Jeong, J. W. (2019). Objective detection of eloquent axonal pathways to minimize postoperative deficits in pediatric epilepsy surgery using diffusion tractography and convolutional neural networks. IEEE Transactions on Medical Imaging, 38(8), 1910–1922.

    Article  Google Scholar 

  • Xue, T., et al. (2023). Superficial white matter analysis: An efficient point-cloud-based deep learning framework with supervised contrastive learning for consistent tractography parcellation across populations and dMRI acquisitions. Medical Image Analysis, 85, 102759.

    Article  PubMed  Google Scholar 

  • Yamada, K., Sakai, K., Akazawa, K., Yuen, S., & Nishimura, T. (2009). MR tractography: A review of its clinical applications. Magnetic Resonance in Medical Sciences, 8(4), 165–174.

    Article  PubMed  Google Scholar 

  • Yamada, N., et al. (2018). Diffusion tensor imaging evaluation of neural network development in patients undergoing therapeutic repetitive transcranial magnetic stimulation following stroke. Neural Plasticity, 2018.

  • Yeatman, J. D., Dougherty, R. F., Myall, N. J., Wandell, B. A., & Feldman, H. M. (2012). Tract Profiles of White Matter Properties: Automating Fiber-Tract Quantification. PLoS One, 7,(11).

  • Yeh, F. C., et al. (2018). Population-averaged atlas of the macroscale human structural connectome and its network topology. NeuroImage, 178, 57–68.

    Article  PubMed  Google Scholar 

  • Yendiki, A., et al. (2011). Automated Probabilistic Reconstruction of White-Matter Pathways in Health and Disease Using an Atlas of the Underlying Anatomy. Frontiers in Neuroinformatics, 5, 23.

  • Yoo, S. W., et al. (2015). An example-based multi-atlas approach to automatic labeling of white matter tracts. PLoS ONE, 10(7), e0133337.

    Article  PubMed  PubMed Central  Google Scholar 

  • Zhang, F., et al. (2018). An anatomically curated fiber clustering white matter atlas for consistent white matter tract parcellation across the lifespan. NeuroImage, 179, 429–447.

    Article  PubMed  Google Scholar 

  • Zhang, F., et al. (2022). Quantitative mapping of the brain’s structural connectivity using diffusion MRI tractography: A review. NeuroImage, 249, 118870.

    Article  PubMed  Google Scholar 

  • Zhang, F., Karayumak, S. C., Hoffmann, N., Rathi, Y., Golby, A. J., & O’Donnell, L. J. (2020). Deep white matter analysis (DeepWMA): fast and consistent tractography segmentation. Medical Image Analysis, 65, p. 101761.

  • Zhang, F., Wu, Y., Norton, I., Rathi, Y., Golby, A. J., & O’Donnell, L. J. (2019). test-retest reproducibility of white matter parcellation using diffusion MRI tractography fiber clustering. Human Brain Mapping, 40(10), 3041–3057.

    PubMed  PubMed Central  Google Scholar 

  • Zhang, Y., et al. (2010). Atlas-guided tract reconstruction for automated and comprehensive examination of the white matter anatomy. NeuroImage, 52(4), 1289–1301.

    Article  PubMed  Google Scholar 

  • Zhong, S., Chen, Z., & Egan, G. (2022). Auto-encoded latent representations of white matter streamlines for quantitative distance analysis. Neuroinformatics, 20, 1105–1120.

    Article  PubMed  PubMed Central  Google Scholar 

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Authors and Affiliations

  1. Control and Intelligent Processing Center of Excellence (CIPCE), School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, 14399, Iran

    Nayereh Ghazi & Hamid Soltanian-Zadeh

  2. Department of Neuroscience, University of Padova, Padova, Italy

    Mohammad Hadi Aarabi

  3. Padova Neuroscience Center (PNC), University of Padova, Padova, Italy

    Mohammad Hadi Aarabi

  4. Medical Image Analysis Laboratory, Departments of Radiology and Research Administration, Henry Ford Health System, Detroit, MI, 48202, USA

    Hamid Soltanian-Zadeh

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All co-authors jointly designed the study. N. Ghazi did the literature search and review and wrote the first draft of manuscript. M.H. Aarabi helped with the preparation of the tables. All co-authors read and revised the first draft of the manuscript. All co-authors reviewed and approved the final version of the manuscript.

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Correspondence to Hamid Soltanian-Zadeh.

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Ghazi, N., Aarabi, M.H. & Soltanian-Zadeh, H. Deep Learning Methods for Identification of White Matter Fiber Tracts: Review of State-of-the-Art and Future Prospective. Neuroinform 21, 517–548 (2023). https://doi.org/10.1007/s12021-023-09636-4

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  • DOI: https://doi.org/10.1007/s12021-023-09636-4

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