DL-BET-A Deep Learning Based Tool for Automatic Brain Extraction from Structural Magnetic Resonance Images in Mice


Brain extraction plays an integral role in image processing pipelines in both human and small animal preclinical MRI studies. Due to lack of state-of-the-art tools for automated brain extraction in rodent research, this step is often performed semi-supervised with manual correction, making it prone to inconsistent results. Here, we perform a multi-model brain extraction study and present a semi-automated preprocessing work ow and deep neural network with a 3D Residual Attention U-Net architecture as the optimal network for automated skull-stripping in neuroimaging analysis pipelines, achieving a DICE score of 0.987 and accuracy of 99.7%.

In International Society for Magnetic Resonance in Medicine (ISMRM) 1-Page Abstract
Chen Liu
Chen Liu
Computer Science PhD Student

Researcher in machine learning + healthcare.