A Machine Learning Based Approach to Fiber Tractography Using Classifier Voting

MRtrix: Diffusion tractography in crossing fiber regions

Tournier, J., Calamante, F., & Connelly, A. (2012) · International Journal of Imaging Systems and Technology · doi:10.1002/ima.22005 · Cited by 1,370 · see citing works

Quantitative evaluation of 10 tractography algorithms on a realistic diffusion MR phantom

Fillard, P., Descoteaux, M., Goh, A., Gouttard, S., Jeurissen, B., Malcolm, J., Ramirez-Manzanares, A., Reisert, M., Sakaie, K., Tensaouti, F., et al. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.01.032 · Cited by 418 · see citing works

Tractometer: Towards validation of tractography pipelines

Côté, M.-A., Girard, G., Boré, A., Garyfallidis, E., Houde, J.-C., & Descoteaux, M. (2013) · Medical Image Analysis · doi:10.1016/j.media.2013.03.009 · Cited by 228 · see citing works

Image Quality Transfer via Random Forest Regression: Applications in Diffusion MRI

Alexander, D. C., Zikic, D., Zhang, J., Zhang, H. G., & Criminisi, A. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-10443-0_29 · Cited by 113 · see citing works

Fiberfox: Facilitating the creation of realistic white matter software phantoms

Neher, P., Laun, F. B., Stieltjes, B., & Maier-Hein, K. H. (2013) · Magnetic Resonance in Medicine · doi:10.1002/mrm.25045 · Cited by 112 · see citing works

MITK Diffusion Imaging

Neher, P., Reicht, I., van Bruggen, T., Goch, C. J., Reisert, M., Nolden, M., Zelzer, S., Meinzer, H.-P., Stieltjes, B., & Fritzsche, K. H. (2012) · Methods of Information in Medicine · doi:10.3414/me11-02-0031 · Cited by 83 · see citing works

Learning a Reliable Estimate of the Number of Fiber Directions in Diffusion MRI

Schultz, T. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-33454-2_61 · Cited by 38 · see citing works

Tractography-based connectomes are dominated by false-positive connections

Maier-Hein, K. H., Neher, P., Houde, J.-C., Côté, M.-A., Garyfallidis, E., Zhong, J., Chamberland, M., Yeh, F.-C., Lin, Y.-C., Ji, Q., et al. (2016) · doi:10.1101/084137 · Cited by 1,361 · see citing works

q-Space Deep Learning: Twelve-Fold Shorter and Model-Free Diffusion MRI Scans

Golkov, V., Dosovitskiy, A., Sperl, J. I., Menzel, M. I., Czisch, M., Sämann, P. G., Brox, T., & Cremers, D. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2551324 · Cited by 316 · see citing works

White matter tractography for neurosurgical planning: A topography-based review of the current state of the art

Essayed, W. I., Zhang, F., Unadkat, P., Cosgrove, G. R., Golby, A. J., & O’Donnell, L. J. (2017) · NeuroImage Clinical · doi:10.1016/j.nicl.2017.06.011 · Cited by 249 · see citing works

Image quality transfer and applications in diffusion MRI

Alexander, D. C., Zikic, D., Ghosh, A., Tanno, R., Wottschel, V., Zhang, J., Kaden, E., Dyrby, T. B., Sotiropoulos, S. N., Zhang, H. G., et al. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.02.089 · Cited by 126 · see citing works

Tractography and machine learning: current state and open challenges

Poulin, P., Jörgens, D., Jodoin, P.-M., & Descoteaux, M. (2019) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2019.04.013 · Cited by 84 · see citing works

Fiber tractography using machine learning

Neher, P. F., Côté, M.-A., Houde, J.-C., Descoteaux, M., & Maier-Hein, K. H. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.07.028 · Cited by 68 · see citing works

Learn to Track: Deep Learning for Tractography

Poulin, P., Côté, M.-A., Houde, J., Petit, L., Neher, P., Maier-Hein, K. H., Larochelle, H., & Descoteaux, M. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-66182-7_62 · Cited by 63 · see citing works

Direct Estimation of Fiber Orientations Using Deep Learning in Diffusion Imaging

Koppers, S., & Merhof, D. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-47157-0_7 · Cited by 43 · see citing works

DeepTract: A Probabilistic Deep Learning Framework for White Matter Fiber Tractography

Benou, I., & Raviv, T. R. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-32248-9_70 · Cited by 44 · see citing works

Track-to-Learn: A general framework for tractography with deep reinforcement learning

Théberge, A., Desrosiers, C., Descoteaux, M., & Jodoin, P. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.102093 · Cited by 43 · see citing works

Learning to estimate the fiber orientation distribution function from diffusion-weighted MRI

Karimi, D., Vasung, L., Jaimes, C. E., Machado‐Rivas, F., Warfield, S. K., & Gholipour, A. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118316 · Cited by 42 · see citing works

Diffusion MRI with machine learning

Karimi, D., & Warfield, S. K. (2024) · Imaging Neuroscience · doi:10.1162/imag_a_00353 · Cited by 23 · see citing works

A machine learning-based method for estimating the number and orientations of major fascicles in diffusion-weighted magnetic resonance imaging

Karimi, D., Vasung, L., Jaimes, C. E., Machado‐Rivas, F., Khan, S., Warfield, S. K., & Gholipour, A. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.102129 · Cited by 19 · see citing works

CTtrack: A CNN+Transformer-based framework for fiber orientation estimation & tractography

Hosseini, S. M., Hassanpour, M., Masoudnia, S., Iraji, S., Raminfard, S., & Nazem-Zadeh, M.-R. (2022) · Neuroscience Informatics · doi:10.1016/j.neuri.2022.100099 · Cited by 18 · see citing works

Noise Level Matching Improves Robustness of Diffusion Mri Parameter Inference by Synthetic Q-Space Learning

Masutani, Y. (2019) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2019.8759161 · Cited by 14 · see citing works

Active Learning for Delineation of Curvilinear Structures

Mosinska, A., Sznitman, R., Głowacki, P., & Fua, P. (2016) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2016.565 · Cited by 11 · see citing works

Learning a Single Step of Streamline Tractography Based on Neural Networks

Jörgens, D., Smedby, Ö., & Rodrigo Moreno, A. (2018) · Mathematics and visualization · doi:10.1007/978-3-319-73839-0_8 · Cited by 10 · see citing works

Active Learning and Proofreading for Delineation of Curvilinear Structures

Mosinska, A., Tarnawski, J., & Fua, P. (2017) · Infoscience (Ecole Polytechnique Fédérale de Lausanne) · Cited by 9 · see citing works

What matters in reinforcement learning for tractography

Théberge, A., Desrosiers, C., Boré, A., Descoteaux, M., & Jodoin, P. (2024) · Medical Image Analysis · doi:10.1016/j.media.2024.103085 · Cited by 8 · see citing works

Active Learning and Proofreading for Delineation of Curvilinear Structures

Mosinska, A., Tarnawski, J., & Fua, P. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-66185-8_19 · Cited by 7 · see citing works

Recent Advances in Parameter Inference for Diffusion MRI Signal Models

Masutani, Y. (2021) · Magnetic Resonance in Medical Sciences · doi:10.2463/mrms.rev.2021-0005 · Cited by 6 · see citing works

Disentangled and Proportional Representation Learning for Multi-view Brain Connectomes

Zhang, Y., Zhan, L., Wu, S., Thompson, P. M., & Huang, H. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-87234-2_48 · Cited by 6 · see citing works

Q-ball high-resolution fiber tractography of language associated tracts: quantitative evaluation of applicability for glioma resections

BECKER, D., Scherer, M., Neher, P., Jungk, C., Jesser, J., Pflüger, I., Bendszus, M., Maier-Hein, K. H., & Unterberg, A. W. (2022) · Journal of Neurosurgical Sciences · doi:10.23736/s0390-5616.19.04782-9 · Cited by 5 · see citing works

Assessment of U-Net in the segmentation of short tracts: Transferring to clinical MRI routine

Konell, H. G., Murta, L. O., dos Santos, A. C., & Salmón, C. E. G. (2024) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2024.05.009 · Cited by 3 · see citing works

Optimizing the linear fascicle evaluation algorithm for many-core systems

Aggarwal, K., & Bondhugula, U. K. R. (2019) · International Conference on Supercomputing · doi:10.1145/3330345.3332469 · Cited by 3 · see citing works

Anatomy-to-tract mapping infers white matter pathways without diffusion streamline propagation

Tan, Y. F., Huynh, K. M., Liu, S., Phan, R. C. ‐., Ting, C., & Yap, P. (2025) · Nature Communications · doi:10.1038/s41467-025-66615-w · Cited by 2 · see citing works

Synthetic Q-space learning with mixture distribution noise for robust DKI parameter inference

Masutani, Y., Fujiwara, T., & Sasaki, K. (2021) · International Forum on Medical Imaging in Asia (IFMIA) · doi:10.1117/12.2590657 · Cited by 2 · see citing works

HyTract: Advancing tractography for neurosurgical planning with a hybrid method integrating neural networks and a path search algorithm

Koryciński, M., Ciecierski, K. A., & Niewiadomska‐Szynkiewicz, E. (2025) · Neural Networks · doi:10.1016/j.neunet.2025.107624 · Cited by 2 · see citing works

A taxonomic guide to diffusion MRI tractography visualization tools

Laamoumi, M., Hendriks, T., & Chamberland, M. (2024) · NMR in Biomedicine · doi:10.1002/nbm.5267 · Cited by 1 · see citing works

Tract-RLFormer: A Tract-Specific RL Policy Based Decoder-Only Transformer Network

Joshi, A., Sharma, A., Goel, A., Jha, R. R., Ahuja, C. K., Bhavsar, A. V., & Nigam, A. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-78201-5_17 · Cited by 1 · see citing works

Semi-Supervised Hybrid Ensemble Learning for Fault Detection in 20kV XLPE Cables

Xu, N. (2025) · Informatica · doi:10.31449/inf.v49i20.8371 · Cited by 1 · see citing works

CL-TrackNet: A Hybrid CNN-LSTM Network for Tractography

Yao, Y., Ai, L., & Gu, W. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.130846 · Cited by 1 · see citing works

Deep-learning based tractography for neonates

Mukherjee, S., Paquette, N., Nelson, M. D., Wang, Y., Wallace, J., Panigrahy, A., & Leporé, N. (2020) · International Symposium on Medical Information Processing and Analysis (SIPAIM) · doi:10.1117/12.2579609 · Cited by 1 · see citing works

Machine learning in tractography

Neher, P., Poulin, P., Jörgens, D., Reisert, M., Benou, I., & Maier-Hein, K. H. (2025) · Elsevier eBooks · doi:10.1016/b978-0-12-818894-1.00032-x · Cited by 1 · see citing works

Transformer-Based Framework for Fiber Orientation Estimation & TractographyPreprint

Hosseini, S.-M. H., Hassanpour, M., Masoudnia, S., Iraji, S., Raminfard, S., & Nazem-Zadeh, M.-R. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4157505 · Cited by 1 · see citing works

Inference of a U-fiber bundle atlas informed by the variability of the cortical folding pattern

Labra, N. (2020) · theses.fr (ABES) · doi:10.70675/42b36629z423cz4f6az9807zf916bfc2242a · Cited by 2 · see citing works

Diffusion MRI Fiber Tractography by Flow Field Formation with Extended Physarum Solver: A Pilot Study with 2D Phantoms

Masutani, Y. (2020) · Mathematics and visualization · doi:10.1007/978-3-030-52893-5_16

ConvTract: A Convolutional-based Framework for TractographyPreprint

Hosseini, S. M. H., Hassanpour, M., Masoudnia, S., Nazem-Zadeh, M.-R., & Asheghabadi, M. H. (2021) · doi:10.36227/techrxiv.16919056.v1

Tractography of White Matter Fibers Using Diffusion MRI

Ramamoorthy, S. A. (2021) · Advances in medical technologies and clinical practice book series · doi:10.4018/978-1-7998-7433-1.ch006

ConvTract: A Convolutional-based Framework for TractographyPreprint

Hosseini, S. M. H., Asheghabadi, M. H., Masoudnia, S., Nazem-Zadeh, M.-R., & Asheghabadi, M. H. (2022) · doi:10.36227/techrxiv.16919056.v3

ConvTract: A Convolutional-based Framework for TractographyPreprint

Hosseini, S. M. H., Asheghabadi, M. H., Masoudnia, S., & Nazem-Zadeh, M.-R. (2021) · doi:10.36227/techrxiv.16919056

ConvTract: A Convolutional-based Framework for TractographyPreprint

Hosseini, S. M. H., Asheghabadi, M. H., Masoudnia, S., Nazem-Zadeh, M.-R., & Asheghabadi, M. H. (2021) · doi:10.36227/techrxiv.16919056.v2

The Application of the Preoperative Image-Guided 3D Visualization Supported by Machine Learning to the Prediction of Organs Reconstruction During Pancreaticoduodenectomy via a Head-Mounted Displays

Proniewska, K. K., Kolecki, R., Grochowska, A., Popiela, T. J., Rogula, T. G., Malinowski, K. P., Dołęga‐Dołegowski, D., Kenig, J., Richter, P., Dąbrowa, J., et al. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-43401-3_21

Non-parametric prediction of brain MRI microstructure using transfer learning

Chau, G. R., Weber, E. M. M., Batra, A., Ni, L., Zeineh, M., Chaudhari, A. S., Adeli, E., Knowles, J. K., & McNab, J. A. (2025) · Imaging Neuroscience · doi:10.1162/imag_a_00548

Global inference of the structural connectivity of white matter fiber bundles using deep learning approaches and microstructural prior knowledge

Popov, A. (2022) · doi:10.70675/6e60fea8z1d9fz4280za2d3z8537c1904282

FiberLM: A Transformer-Based Model for Mouse Brain Diffusion MRI Tractography Guided by Viral Tracer DataPreprint

Wen, R., Zhang, J., & Liang, Z. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.05.06.723316

Tractrlfusion: A GPT-Based Multi-Critic Policy Fusion Framework for Fiber Tractography

Joshi, A., Sharma, A., Goel, A., Jha, R. R., Ahuja, C. K., Bhavsar, A., & Nigam, A. (2026) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi61048.2026.11515392

Fiber tractography using machine learning

Neher, P. F., Côté, M.-A., Houde, J.-C., Descoteaux, M., & Maier-Hein, K. H. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.07.028 · Cited by 68 · see citing works

Investigation of Learning-Based Tractography via TractSeg

Zou, Y. (2023) · Carolina Digital Repository (University of North Carolina at Chapel Hill) · doi:10.17615/1kvk-q396

DeepTract: A Probabilistic Deep Learning Framework for White Matter Fiber Tractography

Benou, I., & Raviv, T. R. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-32248-9_70 · Cited by 44 · see citing works

Reinforcement Tractography: A Hybrid Approach for Robust Segmentation of Complex Fiber Bundles

Cabeen, R. P., & Toga, A. W. (2020) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi45749.2020.9098371 · Cited by 14 · see citing works

A Deep Diffusion Model Approach for Diffusion MRI White Matter Fiber Tractography

Li, Y., Zhang, W., Zhu, X., Wu, Y., Yogesh Rathi, A., O’Donnell, L. J., & Zhang, F. (2025) · Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition · doi:10.58530/2025/4786