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Found 5 papers, 1 papers with code
Supervised Tractogram Filtering using Geometric Deep Learning
A tractogram is a virtual representation of the brain white matter.
FIESTA: Autoencoders for accurate fiber segmentation in tractography
Our proposed method improves bundle segmentation coverage by recovering hard-to-track bundles with generative sampling through the latent space seeding of the subject bundle and the atlas bundle.
Generative Sampling in Bundle Tractography using Autoencoders (GESTA)
GESTA is thus a novel deep generative bundle tractography method that can be used to improve the tractography reconstruction of the white matter.
Filtering in tractography using autoencoders (FINTA)
Current brain white matter fiber tracking techniques show a number of problems, including: generating large proportions of streamlines that do not accurately describe the underlying anatomy; extracting streamlines that are not supported by the underlying diffusion signal; and under-representing some fiber populations, among others.
Tractogram filtering of anatomically non-plausible fibers with geometric deep learning
The intuitive idea is to model a fiber as a point cloud and the goal is to investigate whether and how a geometric deep learning model might capture its anatomical properties.
