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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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“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]
Citations
Cites
R: A Language and Environment for Statistical Computing
R Core Team (2000) · doi:10.32614/r.manuals · Cited by 355,893
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
Fully convolutional networks for semantic segmentation
Long, J., Shelhamer, E., & Darrell, T. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298965 · Cited by 38,834
(2010) · Cited by 14,134
(2010) · International Conference on Machine Learning · Cited by 13,995
Measures of the Amount of Ecologic Association Between Species
Dice, L. R. (1945) · Ecology · doi:10.2307/1932409 · Cited by 12,187
Scale-space and edge detection using anisotropic diffusion
Perona, P., & Malik, J. (1990) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.56205 · Cited by 12,143
Fully convolutional networks for semantic segmentation
Shelhamer, E., Long, J., & Darrell, T. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2572683 · Cited by 11,898
Jenkinson, M., Bannister, P., Brady, M., & Smith, S. (2002) · NeuroImage · doi:10.1006/nimg.2002.1132 · Cited by 11,888
Fast robust automated brain extraction
Smith, S. M. (2002) · Human Brain Mapping · doi:10.1002/hbm.10062 · Cited by 10,496
Fischl, B. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.01.021 · Cited by 9,526
3D Slicer as an image computing platform for the Quantitative Imaging Network
Fedorov, A., Beichel, R., Kalpathy–Cramer, J., Finet, J., Fillion‐Robin, J., Pujol, S., Bauer, C., Jennings, D. L., Fennessy, F., Milan Sonka, A., et al. (2012) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2012.05.001 · Cited by 9,095
Zhang, Y., Brady, M., & Smith, S. M. (2001) · IEEE Transactions on Medical Imaging · doi:10.1109/42.906424 · Cited by 7,628
The multimodal brain tumor image segmentation benchmark (BRATS)
Menze, B. H., Jakab, A., Bauer, S., Kalpathy-Cramer, J., Farahani, K., Kirby, J., Burren, Y., Porz, N., Slotboom, J., Wiest, R., et al. (2015) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2014.2377694 · Cited by 6,781
He, K., Sun, J., & Tang, X. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2012.213 · Cited by 5,546
A nonparametric method for automatic correction of intensity nonuniformity in MRI data
Sled, J. G., Zijdenbos, A. P., & Evans, A. C. (1998) · IEEE Transactions on Medical Imaging · doi:10.1109/42.668698 · Cited by 4,731
The Alzheimer’s disease neuroimaging initiative (ADNI): MRI methods
Jack, C. R., Bernstein, M. A., Fox, N. C., Thompson, P., Alexander, G., Harvey, D., Borowski, B., Britson, P. J., L. Whitwell, J., Ward, C., et al. (2008) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.21049 · Cited by 4,347
Elastix: a toolbox for intensity-based medical image registration
Klein, S., Staring, M., Murphy, K., Viergever, M. A., & Pluim, J. (2010) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2009.2035616 · Cited by 4,311
A hybrid approach to the skull stripping problem in MRI
Ségonne, F., Dale, A. M., Busa, E., Glessner, M., Salat, D. H., Hahn, H., & Fischl, B. R. (2004) · NeuroImage · doi:10.1016/j.neuroimage.2004.03.032 · Cited by 2,227
Marcus, D. S., Wang, T. H., Parker, J., Csernansky, J. G., Morris, J. C., & Buckner, R. L. (2007) · Journal of Cognitive Neuroscience · doi:10.1162/jocn.2007.19.9.1498 · Cited by 2,163
Warfield, S. K., Zou, K. H., & Wells, W. M. (2004) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2004.828354 · Cited by 2,072
MRI-PET Registration with Automated Algorithm
Woods, R. P., Mazziotta, J. C., & Cherry, A. S. R. (1993) · Journal of Computer Assisted Tomography · doi:10.1097/00004728-199307000-00004 · Cited by 1,636
The Alzheimer's Disease Neuroimaging Initiative
Mueller, S. G., Weiner, M. W., Thal, L. J., Petersen, R. C., Jack, C., Jagust, W., Trojanowski, J. Q., Toga, A. W., & Beckett, L. (2005) · Neuroimaging Clinics of North America · doi:10.1016/j.nic.2005.09.008 · Cited by 1,525
Construction of a 3D probabilistic atlas of human cortical structures
Shattuck, D. W., Mirza, M., Adisetiyo, V., Hojatkashani, C., Salamon, G., Narr, K. L., Poldrack, R. A., Bilder, R. M., & Toga, A. W. (2007) · NeuroImage · doi:10.1016/j.neuroimage.2007.09.031 · Cited by 1,208
Theano: new features and speed improvements
Bastien, F., Lamblin, P., Pascanu, R., Bergstra, J., Goodfellow, I., Bergeron, A., Bouchard, N., Warde-Farley, D., & Bengio, Y. (2012) · arXiv · doi:10.48550/arxiv.1211.5590 · Cited by 1,005
Magnetic Resonance Image Tissue Classification Using a Partial Volume Model
Shattuck, D. W., Sandor-Leahy, S. R., Schaper, K. A., Rottenberg, D. A., & Leahy, R. M. (2001) · NeuroImage · doi:10.1006/nimg.2000.0730 · Cited by 951
Automated 3-D Extraction of Inner and Outer Surfaces of Cerebral Cortex from MRI
MacDonald, D. J., Kabani, N. J., Avis, D., & Evans, A. C. (2000) · NeuroImage · doi:10.1006/nimg.1999.0534 · Cited by 850
Deep learning for neuroimaging: a validation study
Plis, S., Hjelm, D., Salakhutdinov, R., Allen, E. A., Bockholt, H. J., Long, J. D., Johnson, H. J., Paulsen, J. S., Turner, J. A., & Calhoun, V. D. (2014) · Frontiers in Neuroscience · doi:10.3389/fnins.2014.00229 · Cited by 642
Robust Brain Extraction Across Datasets and Comparison With Publicly Available Methods
Iglesias, J. E., Liu, C., Thompson, P. M., & Tu, Z. (2011) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2011.2138152 · Cited by 580
Symmetric Atlasing and Model Based Segmentation: An Application to the Hippocampus in Older Adults
Grabner, G., Janke, A. L., Budge, M. M., Smith, D. B., Pruessner, J. C., & Collins, D. L. (2006) · Lecture notes in computer science · doi:10.1007/11866763_8 · Cited by 556
BEaST: Brain extraction based on nonlocal segmentation technique
Eskildsen, S. F., Coupé, P., Fonov, V., Manjón, J. V., Leung, K. K., Guizard, N., Wassef, S. N., Østergaard, L. R., & Collins, D. L. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.09.012 · Cited by 486
Cortical Change in Alzheimer's Disease Detected with a Disease-specific Population-based Brain Atlas
Thompson, P. M., Mega, M. S., Woods, R. P., Zoumalan, C. I., Lindshield, C., Blanton, R. E., Moussai, J., Holmes, C. J., Cummings, J. L., & Toga, A. W. (2001) · Cerebral Cortex · doi:10.1093/cercor/11.1.1 · Cited by 447
Torsten Rohlfing, B. (2011) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2011.2163944 · Cited by 405
(2016) · F1000Research Channels · doi:10.12688/f1000research.channels.118 · Cited by 309
Evaluation of volume-based and surface-based brain image registration methods
Klein, A., Ghosh, S. S., Avants, B., Yeo, B. T. T., Fischl, B., Ardekani, B., Gee, J. C., Mann, J. J., & Parsey, R. V. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.01.091 · Cited by 255
Fennema‐Notestine, C., Özyurt, İ. B., Clark, C. P., Morris, S., Bischoff‐Grethe, A., Bondi, M. W., Jernigan, T. L., Fischl, B. R., Ségonne, F., Shattuck, D. W., et al. (2005) · Human Brain Mapping · doi:10.1002/hbm.20161 · Cited by 191
(2014) · Cited by 188
Self-citationOnline resource for validation of brain segmentation methods
Shattuck, D. W., Prasad, G., Mirza, M., Narr, K. L., & Toga, A. W. (2008) · NeuroImage · doi:10.1016/j.neuroimage.2008.10.066 · Cited by 167
Accuracy and reproducibility study of automatic MRI brain tissue segmentation methods
de Boer, R., Vrooman, H. A., Ikram, M. A., Vernooij, M. W., Breteler, M. M. B., van der Lugt, A., & Niessen, W. J. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.03.012 · Cited by 134
A meta-algorithm for brain extraction in MRI
Rex, D., Shattuck, D. W., Woods, R. P., Narr, K. L., Eileen Luders, A., Rehm, K., Stolzner, S. E., Rottenberg, D. A., & Toga, A. W. (2004) · NeuroImage · doi:10.1016/j.neuroimage.2004.06.019 · Cited by 117
Wang, Y., Nie, J., Yap, P., Li, G., Shi, F., Geng, X., Guo, L., Shen, D. G., & Initiative, A. D. N. (2014) · PLoS ONE · doi:10.1371/journal.pone.0077810 · Cited by 93
A fast learning algorithm for image segmentation with max-pooling convolutional networks
Masci, J., Giusti, A., Ciresan, D. C., Fricout, G., & Schmidhuber, J. (2013) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2013.6738559 · Cited by 76
Galdames, F. J., Jaillet, F., & Perez, C. A. (2012) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2012.02.017 · Cited by 76
Wang, L., Chen, Y., Pan, X., Hong, X., & Xia, D. (2010) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2010.03.004 · Cited by 64
Cortical reconstruction using implicit surface evolution: Accuracy and precision analysis
Tosun, D., Rettmann, M. E., Naiman, D. Q., Resnick, S. M., Kraut, M. A., & Prince, J. L. (2005) · NeuroImage · doi:10.1016/j.neuroimage.2005.08.061 · Cited by 39
Zhao, L., Ruotsalainen, U., Hirvonen, J., Hietala, J. A., & Tohka, J. (2010) · Medical Image Analysis · doi:10.1016/j.media.2010.02.001 · Cited by 30
Robust Skull Stripping of Clinical Glioblastoma Multiforme Data
Speier, W. F., Iglesias, J. E., El-Kara, L., Zhuowen Tu, A., & Arnold, C. W. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-23626-6_81 · Cited by 27
A Fast Learning Algorithm for Image Segmentation with Max-Pooling Convolutional NetworksPreprint
Masci, J., Giusti, A., Cireşan, D., et al. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1302.1690 · Cited by 15
High-resolution intersubject averaging and a coordinate system for the cortical surface
Fischl, B., Sereno, M. I., Tootell, R. B. H., & Dale, A. M. (1999) · Human Brain Mapping · doi:10.1002/(sici)1097-0193(1999)8:4
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Research works printed but not linked: 1
- Menze, 2014, The multimodal brain tumorimage segmentation benchmark (BRATS), IEEE Trans. Med. Imaging
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Sorted by citations
A survey on deep learning in medical image analysis
Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M., van der Laak, J. A. W. M., van Ginneken, B., & Sánchez, C. I. (2017) · Medical Image Analysis · doi:10.1016/j.media.2017.07.005 · Cited by 15,332
3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation
Çiçek, Ö., Abdulkadir, A., Lienkamp, S. S., Brox, T., & Ronneberger, O. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46723-8_49 · Cited by 7,945
Deep learning in medical image analysis
Shen, D., Wu, G., & Suk, H.-I. (2017) · Annual Review of Biomedical Engineering · doi:10.1146/annurev-bioeng-071516-044442 · Cited by 4,968
An overview of deep learning in medical imaging focusing on MRI
Lundervold, A. S., & Lundervold, A. (2019) · Zeitschrift für Medizinische Physik · doi:10.1016/j.zemedi.2018.11.002 · Cited by 2,058
Deep Learning for Health Informatics
Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., & Yang, G.-Z. (2017) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2016.2636665 · Cited by 2,000
Metadata
- 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
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › 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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