Track-To-Learn: A general framework for tractography with deep reinforcement learning

/in , , /by
Track-To-Learn: A general framework for tractography with deep reinforcement learning

Théberge, A and Desrosiers, C and Descoteaux, M and Jodoin, P M

bioRxiv 2020

Abstract : Diffusion MRI tractography is currently the only non-invasive tool able to assess the white-matter structural connectivity of a brain. Since its inception, it has been widely documented that tractography is prone to producing erroneous tracks while missing true positive łdots

You might also like
Deep learning forecasts the occurrence of sleep apnea from single-lead ECG
Context-aware virtual adversarial training for anatomically-plausible segmentation
Multi-region segmentation of bladder cancer structures in MRI with progressive dilated convolutional networks
Digital radiography segmentation of scoliotic vertebral pedicles using morphological segmentation
A deep learning classification scheme based on augmented-enhanced features to segment organs at risk on the optic region in brain cancer patients
Introductory background
Estimating accurate multi-class probabilities with support vector machines
Dielectric geometry for capacitive-based tactile sensor