Fig. 2
Pipeline of the proposed method; blocks of machine learning appear in orange and non-learning methods appear in blue. Input T2-weighted MRIs in native space are first preprocessed (N4 bias field inhomogeneity corrected, and registration to a Montreal Neurological Institute (MNI) template using non-rigid transformation), (A) The first block of the segmentation method aims to extract the peri-sinus mask (combined background or parasagittal space and arachnoid granulation), (B) A is Gaussian mixture model is fit to data using an expectation maximum (EM) algorithm to estimate the maximum a posteriori probability distribution and assign a label for each voxel (i.e., parasagittal dural space or sinus), (C) A second U-net is then used to label arachnoid granulation and correct miss-labeled parasagittal dural space voxels. The final label map is transformed back to native space using inverse transform