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Found 12 papers, 3 papers with code
TractOracle: towards an anatomically-informed reward function for RL-based tractography
Reinforcement learning (RL)-based tractography is a competitive alternative to machine learning and classical tractography algorithms due to its high anatomical accuracy obtained without the need for any annotated data.
Merging multiple input descriptors and supervisors in a deep neural network for tractogram filtering
Their outputs are combined to obtain the classification labels for the streamlines.
What Matters in Reinforcement Learning for Tractography
Recently, deep reinforcement learning (RL) has been proposed to learn the tractography procedure and train agents to reconstruct the structure of the white matter without manually curated reference streamlines.
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.
Manifold-aware Synthesis of High-resolution Diffusion from Structural Imaging
The physical and clinical constraints surrounding diffusion-weighted imaging (DWI) often limit the spatial resolution of the produced images to voxels up to 8 times larger than those of T1w images.
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.
Medical Imaging with Deep Learning: MIDL 2020 -- Short Paper Track
This compendium gathers all the accepted extended abstracts from the Third International Conference on Medical Imaging with Deep Learning (MIDL 2020), held in Montreal, Canada, 6-9 July 2020.
Understanding Alzheimer disease’s structural connectivity through explainable AI
In the following work, we use a modified version of deep BrainNet convolutional neural network (CNN) trained on the diffusion weighted MRI (DW-MRI) tractography connectomes of patients with Alzheimer’s Disease (AD) and Mild Cognitive Impairment (MCI) to better understand the structural connectomics of that disease.
Tractography and machine learning: Current state and open challenges
Supervised machine learning (ML) algorithms have recently been proposed as an alternative to traditional tractography methods in order to address some of their weaknesses.
NODDI-SH: a computational efficient NODDI extension for fODF estimation in diffusion MRI
Exploiting the ability of Spherical Harmonics (SH) to model spherical functions, we propose a new reconstruction model for DMRI data which is able to estimate both the fiber Orientation Distribution Function (fODF) and the relative volume fractions of the neurites in each voxel, which is robust to multiple fiber crossings.
Non Local Spatial and Angular Matching : Enabling higher spatial resolution diffusion MRI datasets through adaptive denoising
We then introduce a spatially and angular adaptive denoising technique, the Non Local Spatial and Angular Matching (NLSAM) algorithm.
