title: Deep MRI brain extraction: A 3D convolutional neural network for skull stripping.
abstract: Brain extraction from magnetic resonance imaging (MRI) is crucial for many neuroimaging workflows. Current methods demonstrate good results on non-enhanced T1-weighted images, but struggle when confronted with other modalities and pathologically altered tissue. In this paper we present a 3D convolutional deep learning architecture to address these shortcomings. In contrast to existing methods, we are not limited to non-enhanced T1w images. When trained appropriately, our approach handles an arbitrary number of modalities including contrast-enhanced scans. Its applicability to MRI data, comprising four channels: non-enhanced and contrast-enhanced T1w, T2w and FLAIR contrasts, is demonstrated on a challenging clinical data set containing brain tumors (N=53), where our approach significantly outperforms six commonly used tools with a mean Dice score of 95.19. Further, the proposed method at least matches state-of-the-art performance as demonstrated on three publicly available data sets: IBSR, LPBA40 and OASIS, totaling N=135 volumes. For the IBSR (96.32) and LPBA40 (96.96) data set the convolutional neuronal network (CNN) obtains the highest average Dice scores, albeit not being significantly different from the second best performing method. For the OASIS data the second best Dice (95.02) results are achieved, with no statistical difference in comparison to the best performing tool. For all data sets the highest average specificity measures are evaluated, whereas the sensitivity displays about average results. Adjusting the cut-off threshold for generating the binary masks from the CNN's probability output can be used to increase the sensitivity of the method. Of course, this comes at the cost of a decreased specificity and has to be decided application specific. Using an optimized GPU implementation predictions can be achieved in less than one minute. The proposed method may prove useful for large-scale studies and clinical trials.
authors.affiliation: MDMI Lab, Division of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany; Junior Group Medical Image Computing, German Cancer Research Center, Heidelberg, Germany; Heidelberg University HCI/IWR, Heidelberg, Germany; Division of Radiology, German Cancer Research Center, Heidelberg, Germany. Electronic address: kleesiek@uni-heidelberg.de.
authors.forename: Jens
authors.initials: J
authors.lastname: Kleesiek
authors.affiliation: MDMI Lab, Division of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany.
authors.forename: Gregor
authors.initials: G
authors.lastname: Urban
authors.affiliation: MDMI Lab, Division of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany.
authors.forename: Alexander
authors.initials: A
authors.lastname: Hubert
authors.affiliation: MDMI Lab, Division of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany.
authors.forename: Daniel
authors.initials: D
authors.lastname: Schwarz
authors.affiliation: Junior Group Medical Image Computing, German Cancer Research Center, Heidelberg, Germany.
authors.forename: Klaus
authors.initials: K
authors.lastname: Maier-Hein
authors.affiliation: MDMI Lab, Division of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany.
authors.forename: Martin
authors.initials: M
authors.lastname: Bendszus
authors.affiliation: MDMI Lab, Division of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany; Division of Radiology, German Cancer Research Center, Heidelberg, Germany.
authors.forename: Armin
authors.initials: A
authors.lastname: Biller
mesh_terms: D001932:Brain Neoplasms / Q000000981:diagnostic imaging*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D000069550:Machine Learning; D008279:Magnetic Resonance Imaging / Q000379:methods; D016571:Neural Networks, Computer; D059906:Neuroimaging / Q000379:methods*; D012886:Skull
keywords: Brain extraction; Brain mask; Convolutional networks; Deep learning; MRI; Skull stripping