Abstract
The combined effort of brain anatomy experts and computerized methods has continuously improved the quality of available gold-standard tractograms for diffusion-weighted MRI. These prototypical tractograms contain information that can be utilized by other brain mapping applications. However, this transfer requires data-driven tractography algorithms, which learn from example tractograms, to deliver the obtained knowledge to other diffusion-weighted MRI data. The value of these data-driven methods would be greatly enhanced, if they could also estimate and control the uncertainty of their predictions. These reasons lead us to propose a generic machine learning method for probabilistic tractography. We demonstrate the general approach with a basic Fisher-von-Mises distribution to model local fiber direction. The distributional parameters are inferred from diffusion data by a neural network. For training the neural network, we derive an analytic, entropy-regularized cost function, which allows to control model uncertainty in accordance with the level of noise in the data. We highlight the ability of our method to quantify the probability of a given fiber, which makes it a useful tool for outlier detection. The tracking performance of the model is evaluated on the ISMRM 2015 Tractography Challenge.
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Notes
- 1.
Code available at https://github.com/vwegmayr/entrack.
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Wegmayr, V., Giuliari, G., Buhmann, J.M. (2019). Entrack: A Data-Driven Maximum-Entropy Approach to Fiber Tractography. In: Fink, G., Frintrop, S., Jiang, X. (eds) Pattern Recognition. DAGM GCPR 2019. Lecture Notes in Computer Science(), vol 11824. Springer, Cham. https://doi.org/10.1007/978-3-030-33676-9_16
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