Deepbet

The segmentation of brain tissues in magnetic resonance imaging (MRI) data is a crucial step in neuroimaging preprocessing pipelines. The advent of deep learning has revolutionized image segmentation, offering high precision with minimal computational requirements. Traditional methods for brain extraction are gradually being replaced by deep learning-based approaches. The research project deepbet addresses this shift, aiming to develop a fast and high-precision brain extraction tool.

Research Project

deepbet utilizes a unique dataset comprising 7837 T1-weighted (T1w) MR images from 191 OpenNeuro datasets. Employing cutting-edge deep learning methods, the project introduces a novel brain extraction tool based on the LinkNet architecture within a two-stage prediction process. This design enhances segmentation performance, establishing a state-of-the-art benchmark during cross-validation. Notably, deepbet achieves a median Dice score (DSC) of 99.0 on unseen datasets, surpassing current best performing deep learning (DSC = 97.8) and classic (DSC = 96.4) methods.

Unlike existing methods that may be sensitive to outliers, deepbet consistently attains a Dice score of > 97.4 across all 7837 images from diverse datasets. Furthermore, the model significantly accelerates the brain extraction process, achieving a speedup of ≈10 times compared to current methods. This enables the processing of one image in ≈2 seconds on low-level hardware. Convenient installation is facilitated through “pip install deepbet,” and the tool is publicly accessible via https://github.com/wwu-mmll/deepbet .