Accurate skull stripping helps following neuro-image analysis. For computer-aided methods, the presentence of the brain skull in structural MRI impacts brain tissue identification, which could result in serious misjudgment, especially for patients with brain tumors. Though there are some existing works on skull stripping in literature, most of them either focus on healthy brain MRI or only apply for a single image modality. These methods may be not optimal for multiparametric MRI scans. In the paper, we propose an ensemble neural network (EnNet), a 3D convolutional neural network (3DCNN) based method, for brain extraction of multiparametric brain MRI scans. We comprehensively investigate the skull stripping performance by using the proposed method on a total of 15 image modality combinations. The comparison shows that using all modalities provides the best performance on skull stripping. We have collected a retrospective dataset of 815 cases with glioblastoma at the University of Pittsburgh Medical Center (UPMC) and The Cancer Imaging Archive (TCIA). The ground truths of the skull stripping are verified by at least one qualified radiologist. The quantitative evaluation gives an average dice score coefficient and Hausdorff distance at 95th percentile, respectively. We also compare the performance to the state-of-the-art methods/tools. The proposed method offers the best performance.
The contributions of the work have five folds: First, the proposed method is a fully automatic end-to-end for skull stripping using a 3D deep learning method. Second, it is applicable for multiparametric MRI (mpMRIs) and is also easy to customize for a single MRI modality. Third, the proposed method not only works for healthy brain mpMRIs but also pre-/post-operative brain mpMRIs with GBM. Fourth, the proposed method is capable to handle multicenter data. Last, to the best of our knowledge, we are the first group to quantitatively compare the skull stripping performance using different modalities.