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Fig. 1 | Brain Informatics

Fig. 1

From: Fast cortical surface reconstruction from MRI using deep learning

Fig. 1

Schematic of the fast cortical surface reconstruction (FastCSR) workflow. The FastCSR workflow can be summarized in four steps: a original T1w images are normalized and fed to a 3D U-Net for segmentation of white matter from gray matter. b After segmentation, hemispheric white matter masks are generated, distinguishing the two hemispheres in the T1w images. c The hemispheric masks and the original T1w images are fed to another 3D U-Net to predict the level set representation of the cortical surface. Level set is an implicit representation of the cortical surface. The U-shaped network architecture can be briefly described as follows: each blue arrow represents a 3 × 3 × 3 convolution process followed by a leaky rectified linear unit (LReLU). Each orange downward arrow indicates a 2 × 2 × 2 max pooling. Each red upward arrow indicates a 2 × 2 × 2 upsampling followed by a convolution (Up-Conv). The long-range skip connections using the copy-and-concatenate (Copy & Concat) operation are indicated by green arrows. d The level set representations of the surfaces are generated by the deep learning model. The voxels whose level set value equals to zero delineate the boundary of the cortical surface. Negative voxels indicated by dark colors are below the surface and positive voxels indicated by light colors are above the surface. The red box shows the level set representation in the left frontal cortex magnified to better visualize the details in the surface boundary. e An explicit surface mesh is reconstructed from the level set representation through a fast topology-preserving isosurface extraction algorithm. The resulting surface is visualized using a dorsal orientation

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