Journal Article

A general skull stripping of multiparametric brain MRIs using 3D convolutional neural network

Pei, Linmin, Ak, Murat, Ak, Nursima, Alkarawi, Safa, Chen, Lingling, Er, Mehmet, Tahon, Nourel Hoda M., Kamal, Abdallah, Zenkin, Serafettin, Colen, Rivka, Yilmaz, Mahir

2022

Overview

Accurate skull stripping facilitates following neuro-image analysis. For computer-aided methods, the presence of brain skull in structural magnetic resonance imaging (MRI) impacts brain tissue identification, which could result in serious misjudgments, specifically for patients with brain tumors. Though there are several existing works on skull stripping in literature, most of them either focus on healthy brain MRIs 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 on multiparametric MRI scans (mpMRIs). We comprehensively investigate the skull stripping performance by using the proposed method on a total of 15 image modality combinations. The comparison shows that utilizing all modalities provides the best performance on skull stripping. We have collected a retrospective dataset of 815 cases with/without glioblastoma multiforme (GBM) 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 the 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 mpMRIs and is also easy to customize for any MRI modality combination. Third, the proposed method not only works for healthy brain mpMRIs but also pre-/post-operative brain mpMRIs with GBM. Fourth, the proposed method handles multicenter data. Finally, to the best of our knowledge, we are the first group to quantitatively compare the skull stripping performance using different modalities. All code and pre-trained model are available at: https://github.com/plmoer/skull_stripping_code_SR .

• Publisher: Nature Publishing Group UK,Nature Publishing Group,Nature Portfolio
• Subject: 631/114/2397 / 692/4028/67/2321 / Algorithms / Brain - diagnostic imaging / Brain - pathology / Brain cancer / Brain tumors / Deep learning / Glioblastoma / Glioblastoma - diagnostic imaging / Glioblastoma - pathology / Humanities and Social Sciences / Humans / Image processing / Image Processing, Computer-Assisted - methods / Magnetic resonance imaging / Magnetic Resonance Imaging - methods / multidisciplinary / Multiparametric Magnetic Resonance Imaging / Neural networks / Neural Networks, Computer / Neuroimaging / Retrospective Studies / Science / Science (multidisciplinary) / Skull / Skull - diagnostic imaging / Skull - pathology