Skull-stripping is an essential pre-processing step in computational neuro-imaging directly impacting subsequent analyses. Existing skull-stripping methods have primarily targeted non-pathologicallyaffected brains. Accordingly, they may perform suboptimally when applied on brain Magnetic Resonance Imaging (MRI) scans that have clearly discernible pathologies, such as brain tumors. Furthermore, existing methods focus on using only T1-weighted MRI scans, even though multi-parametric MRI (mpMRI) scans are routinely acquired for patients with suspected brain tumors. Here we present a performance evaluation of publicly available implementations of established 3D Deep Learning architectures for semantic segmentation (namely DeepMedic, 3D U-Net, FCN), with a particular focus on identifying a skull-stripping approach that performs well on brain tumor scans, and also has a low computational footprint. We have identified a retrospective dataset of 1,796 mpMRI brain tumor scans, with corresponding manually-inspected and verified gold-standard brain tissue segmentations, acquired during standard clinical practice under varying acquisition protocols at the Hospital of the University of Pennsylvania. Our quantitative evaluation identified DeepMedic as the best performing method (Dice = 97.9, Hausdorf f 95 = 2.68). We release this pre-trained model through the Cancer Imaging Phenomics Toolkit (CaPTk) platform.
Keywords: Brain extraction; Brain tumor; CaPTk; Deep learning; DeepMedic; FCN; GBM; Glioblastoma; Skull-stripping; U-Net.