ArticleScientific data2022
TractoInferno - A large-scale, open-source, multi-site database for machine learning dMRI tractography.
Article in Scientific data, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
22 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A systematic review of automated methods to perform white matter tract segmentation.Frontiers in neuroscience · 2024Pooled it
- Benchmarking Generalizability in Deep Learning-Based White Matter Tract Segmentation.bioRxiv : the preprint server for biology · 2026Article
- A large-scale heterogeneous 3D magnetic resonance brain imaging dataset for self-supervised learning.Scientific data · 2026Article
- Anatomy-to-tract mapping infers white matter pathways without diffusion streamline propagation.Nature communications · 2025Article
- Anatomy-to-Tract Mapping Infers White Matter Pathways Without Diffusion Streamline Propagation.bioRxiv : the preprint server for biology · 2025Article
- Anatomical insights into the superior longitudinal system from integrative in- vivo and ex-vivo mapping.Communications biology · 2025Article
- A review on learning-based algorithms for tractography and human brain white matter tracts recognition.Neuroradiology · 2025Review
- Assessing biological self-organization patterns using statistical complexity characteristics: a tool for diffusion tensor imaging analysis.Magma (New York, N.Y.) · 2025Article
- Tutorial: a guide to diffusion MRI and structural connectomics.Nature protocols · 2025Review
- Microstructural mapping of neural pathways in Alzheimer's disease using macrostructure-informed normative tractometry.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Article
- Streamline tractography of the fetal brain in utero with machine learning.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Article
- DIFFERENTIABLE VQ-VAE'S FOR ROBUST WHITE MATTER STREAMLINE ENCODINGS.Proceedings. IEEE International Symposium on Biomedical Imaging · 2024Article
- Microstructural Mapping of Neural Pathways in Alzheimer's Disease using Macrostructure-Informed Normative Tractometry.bioRxiv : the preprint server for biology · 2024Article
- Integrating direct electrical brain stimulation with the human connectome.Brain : a journal of neurology · 2024Article
- Deep Normative Tractometry for Identifying Joint White Matter Macro- and Micro-structural Abnormalities in Alzheimer's Disease.bioRxiv : the preprint server for biology · 2024Article
- A Survey of Publicly Available MRI Datasets for Potential Use in Artificial Intelligence Research.Journal of magnetic resonance imaging : JMRI · 2024Review
- Fibre orientation atlas guided rapid segmentation of white matter tracts.Human brain mapping · 2024Article
- Diffusion MRI with Machine Learning.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Deep Learning Methods for Identification of White Matter Fiber Tracts: Review of State-of-the-Art and Future Prospective.Neuroinformatics · 2023Review
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
TractoInferno is the world's largest open-source multi-site tractography database, including both research- and clinical-like human acquisitions, aimed specifically at machine learning tractography approaches and related ML algorithms. It provides 284 samples acquired from 3 T scanners across 6 different sites. Available data includes T1-weighted images, single-shell diffusion MRI (dMRI) acquisitions, spherical harmonics fitted to the dMRI signal, fiber ODFs, and reference streamlines for 30 delineated bundles generated using 4 tractography algorithms, as well as masks needed to run tractography algorithms. Manual quality control was additionally performed at multiple steps of the pipeline. We showcase TractoInferno by benchmarking the learn2track algorithm and 5 variations of the same recurrent neural network architecture. Creating the TractoInferno database required approximately 20,000 CPU-hours of processing power, 200 man-hours of manual QC, 3,000 GPU-hours of training baseline models, and 4 Tb of storage, to produce a final database of 350 Gb. By providing a standardized training dataset and evaluation protocol, TractoInferno is an excellent tool to address common issues in machine learning tractography.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.