ArticleFrontiers in neuroimaging2022
DORIS: A diffusion MRI-based 10 tissue class deep learning segmentation algorithm tailored to improve anatomically-constrained tractography.
Article in Frontiers in neuroimaging, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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8 citing papers in PubMed.
- Automated Surface-Based Segmentation of Deep Gray Matter Regions Based on Diffusion Tensor Images Reveals Unique Age Trajectories Over the Healthy Lifespan.NMR in biomedicine · 2026Article
- Deep learning-based Desikan-Killiany parcellation of the brain using diffusion MRI.Scientific reports · 2026Article
- Towards an Informed Choice of Diffusion MRI Image Contrasts for Cerebellar Segmentation.Human brain mapping · 2025Article
- DDEvENet: Evidence-based ensemble learning for uncertainty-aware brain parcellation using diffusion MRI.Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society · 2025Article
- DDParcel: Deep Learning Anatomical Brain Parcellation From Diffusion MRI.IEEE transactions on medical imaging · 2024Article
- DiMANI: diffusion MRI for anatomical nuclei imaging-Application for the direct visualization of thalamic subnuclei.Frontiers in human neuroscience · 2024Article
- Exploring the Potential of Machine Learning Algorithms to Improve Diffusion Nuclear Magnetic Resonance Imaging Models Analysis.Journal of medical physicsArticle
- Learning-based segmentation of diffusion-weighted MR images with arbitraryImaging neuroscience (Cambridge, Mass.)Article
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9 authors.
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Abstract
Modern tractography algorithms such as anatomically-constrained tractography (ACT) are based on segmentation maps of white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF). These maps are generally estimated from a T1-weighted (T1w) image and then registered in diffusion weighted images (DWI) space. Registration of T1w to diffusion space and partial volume estimation are challenging and rarely voxel-perfect. Diffusion-based segmentation would, thus, potentially allow not to have higher quality anatomical priors injected in the tractography process. On the other hand, even if FA-based tractography is possible without T1 registration, the literature shows that this technique suffers from multiple issues such as holes in the tracking mask and a high proportion of generated broken and anatomically implausible streamlines. Therefore, there is an important need for a tissue segmentation algorithm that works directly in the native diffusion space. We propose
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