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.
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New methods for preprocessing routines are arising, including deep learning based registration, skull stripping, and noise reduction.
“At the same time, the new methods for these preprocessing routines are also arising, including deep learning based registration [23], skull stripping [24], and noise reduction [25].”
From Deep learning for brain MRI segmentation: state of the art and future directions (2017) · cites this paper as [24]
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“Recently, 3D convolutional neural networks have been also proposed for Alzheimer's disease classification (Payan and Montana, 2015; Sarraf and Tofighi, 2016), brain lesion segmentation (Kamnitsas et al., 2017) and skull stripping (Kleesiek et al., 2016).”
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- Authors
- Kleesiek, J., Urban, G., Hubert, A., Schwarz, D., Maier-Hein, K., Bendszus, M., & Biller, A.
- Affiliations
- German Cancer Research Center (DE)
- University Hospital Heidelberg (DE)
- Corresponding author
- Kleesiek, Jens
- ORCID
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2016-04
- Volume
- 129
- Pages
- 460-469
- ISSN
- 1053-8119 (ISSN-L)
- OpenAlex
- W2284198383
- Semantic Scholar
- 5e10a999925907c8018a10a338a3734e111c6c70
- Cited by
- 505
- References
- 50
- Citations by year
- 2026: 17 · 2025: 28 · 2024: 35 · 2023: 61 · 2022: 72 · 2021: 62 · 2020: 68 · 2019: 74 · 2018: 48 · 2017: 31 · 2016: 9
- FWCI
- 36.41
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- NeuroImage (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Life Sciences › Neuroscience › Neurology › Brain Tumor Detection and Classification
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
- Keywords
- brain extraction, skull stripping, 3D convolutional neural networks, contrast-enhanced MRI, multimodal MRI, FLAIR, T2-weighted imaging, OASIS
- MeSH terms
- Machine Learning, Brain Neoplasms, Humans, Image Enhancement, Image Interpretation, Computer-Assisted, Magnetic Resonance Imaging, Skull, Neural Networks, Computer, Imaging, Three-Dimensional, Neuroimaging
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