Tract-RLFormer: A Tract-Specific RL policy based Decoder-only Transformer Network
This addresses the challenge of false positives in mapping brain white matter pathways for neurological applications, representing an incremental improvement over existing deep learning methods.
The authors tackled the problem of inaccurate fiber tractography in neuroimaging by proposing Tract-RLFormer, a two-stage policy refinement network combining supervised and reinforcement learning, which improved accuracy and generalizability across multiple datasets like TractoInferno, HCP, and ISMRM-2015.
Fiber tractography is a cornerstone of neuroimaging, enabling the detailed mapping of the brain's white matter pathways through diffusion MRI. This is crucial for understanding brain connectivity and function, making it a valuable tool in neurological applications. Despite its importance, tractography faces challenges due to its complexity and susceptibility to false positives, misrepresenting vital pathways. To address these issues, recent strategies have shifted towards deep learning, utilizing supervised learning, which depends on precise ground truth, or reinforcement learning, which operates without it. In this work, we propose Tract-RLFormer, a network utilizing both supervised and reinforcement learning, in a two-stage policy refinement process that markedly improves the accuracy and generalizability across various data-sets. By employing a tract-specific approach, our network directly delineates the tracts of interest, bypassing the traditional segmentation process. Through rigorous validation on datasets such as TractoInferno, HCP, and ISMRM-2015, our methodology demonstrates a leap forward in tractography, showcasing its ability to accurately map the brain's white matter tracts.