2 research outputs found
DeepTract: A Probabilistic Deep Learning Framework for White Matter Fiber Tractography
We present DeepTract, a deep-learning framework for estimating white matter
fibers orientation and streamline tractography. We adopt a data-driven approach
for fiber reconstruction from diffusion weighted images (DWI), which does not
assume a specific diffusion model. We use a recurrent neural network for
mapping sequences of DWI values into probabilistic fiber orientation
distributions. Based on these estimations, our model facilitates both
deterministic and probabilistic streamline tractography. We quantitatively
evaluate our method using the Tractometer tool, demonstrating competitive
performance with state-of-the art classical and machine learning based
tractography algorithms. We further present qualitative results of
bundle-specific probabilistic tractography obtained using our method. The code
is publicly available at: https://github.com/itaybenou/DeepTract.git