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.
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.
Bertò, G., et al. (2021). Classifyber, a robust streamline-based linear classifier for white matter bundle segmentation. NeuroImage, 224, 117402.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Labra, N., et al. (2017). Fast automatic segmentation of white matter streamlines based on a multi-subject bundle atlas. Neuroinformatics, 15(1), 71–86.
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.
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.
Li, B., et al. (2020). Neuro4Neuro: A neural network approach for neural tract segmentation using large-scale population-based diffusion imaging. NeuroImage, 218, 116993.
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.
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.
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.
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.
留言 (0)