DeepTract: A Probabilistic Deep Learning Framework for White Matter Fiber Tractography
This addresses fiber tractography in neuroimaging, which is incremental as it shows competitive rather than superior performance.
The authors tackled white matter fiber tractography from diffusion weighted images by developing DeepTract, a deep learning framework that uses a recurrent neural network to estimate probabilistic fiber orientation distributions without assuming a specific diffusion model, demonstrating competitive performance with state-of-the-art methods using the Tractometer tool.
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.