Abstract
Rodent models are increasingly important in translational neuroimaging research. In rodent neuroimaging, particularly magnetic resonance imaging (MRI) studies, brain extraction is a critical data preprocessing component. Current brain extraction methods for rodent MRI usually require manual adjustment of input parameters due to widely different image qualities and/or contrasts. Here we propose a novel method, termed SHape descriptor selected Extremal Regions after Morphologically filtering (SHERM), which only requires a brain template mask as the input and is capable of automatically and reliably extracting the brain tissue in both rat and mouse MRI images. The method identifies a set of brain mask candidates, extracted from MRI images morphologically opened and closed sequentially with multiple kernel sizes, that match the shape of the brain template. These brain mask candidates are then merged to generate the brain mask. This method, along with four other state-of-the-art rodent brain extraction methods, were benchmarked on four separate datasets including both rat and mouse MRI images. Without involving any parameter tuning, our method performed comparably to the other four methods on all datasets, and its performance was robust with stably high true positive rates and low false positive rates. Taken together, this study provides a reliable automatic brain extraction method that can contribute to the establishment of automatic pipelines for rodent neuroimaging data analysis.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Ashburner, J., & Friston, K. J. (2000). Voxel-based Morphometry—The methods. NeuroImage, 11(6), 805–821. https://doi.org/10.1006/nimg.2000.0582.
Avants, B. B., Tustison, N. J., Wu, J., Cook, P. A., & Gee, J. C. (2011). An open source multivariate framework for n-tissue segmentation with evaluation on public data. Neuroinformatics, 9(4), 381–400. https://doi.org/10.1007/s12021-011-9109-y.
Boult, J. K. R., Apps, J. R., Holsken, A., Hutchinson, J. C., Carreno, G., Danielson, L. S., et al. (2018). Preclinical transgenic and patient-derived xenograft models recapitulate the radiological features of human adamantinomatous craniopharyngioma. Brain pathology (Zurich, Switzerland), 28(4), 475–483. https://doi.org/10.1111/bpa.12525.
Cheng, L., Yang, J., Fan, X., & Zhu, Y. (2005). A generalized level set formulation of the Mumford-Shah functional for brain MR image segmentation. Information Processing in Medical Imaging, 418–430. http://www.springerlink.com/index/4y9pt45egw5tccbt.pdf. Accsessed 3 Feb 2019.
Chou, N., Wu, J., Bai Bingren, J., Qiu, A., & Chuang, K.-H. (2011). Robust automatic rodent brain extraction using 3-D pulse-coupled neural networks (PCNN). IEEE Transactions on Image Processing, 20(9), 2554–2564. https://doi.org/10.1109/TIP.2011.2126587.
Cox, R. W. (1996). AFNI: Software for analysis and visualization of functional magnetic resonance Neuroimages. Computers and Biomedical Research, 29(3), 162–173. https://doi.org/10.1006/cbmr.1996.0014.
Delora, A., Gonzales, A., Medina, C. S., Mitchell, A., Mohed, A. F., Jacobs, R. E., & Bearer, E. L. (2016). A simple rapid process for semi-automated brain extraction from magnetic resonance images of the whole mouse head. Journal of Neuroscience Methods, 257, 185–193. https://doi.org/10.1016/j.jneumeth.2015.09.031.
Denic, A., Macura, S. I., Mishra, P., Gamez, J. D., Rodriguez, M., & Pirko, I. (2011). MRI in rodent models of brain disorders. Neurotherapeutics : the Journal of the American Society for Experimental NeuroTherapeutics, 8(1), 3–18. https://doi.org/10.1007/s13311-010-0002-4.
Dopfel, D., & Zhang, N. (2018). Mapping stress networks using functional magnetic resonance imaging in awake animals. Neurobiology of Stress, 9(May), 251–263. https://doi.org/10.1016/j.ynstr.2018.06.002.
Doshi, J., Erus, G., Ou, Y., Gaonkar, B., & Davatzikos, C. (2013). Multi-Atlas Skull-Stripping. Academic Radiology, 20(12), 1566–1576. https://doi.org/10.1016/j.acra.2013.09.010.
Eskildsen, S. F., Coupé, P., Fonov, V., Manjón, J. V., Leung, K. K., Guizard, N., Wassef, S. N., Østergaard, L. R., Collins, D. L., & Alzheimer's Disease Neuroimaging Initiative. (2012). BEaST: Brain extraction based on nonlocal segmentation technique. NeuroImage, 59(3), 2362–2373. https://doi.org/10.1016/j.neuroimage.2011.09.012.
Esteban, O., Markiewicz, C. J., Blair, R. W., Moodie, C. A., Isik, A. I., Erramuzpe, A., Kent, J. D., Goncalves, M., DuPre, E., Snyder, M., Oya, H., Ghosh, S. S., Wright, J., Durnez, J., Poldrack, R. A., & Gorgolewski, K. J. (2019). fMRIPrep: A robust preprocessing pipeline for functional MRI. Nature Methods, 16(1), 111–116. https://doi.org/10.1038/s41592-018-0235-4.
Glasser, M. F., Sotiropoulos, S. N., Wilson, J. A., Coalson, T. S., Fischl, B., Andersson, J. L., Xu, J., Jbabdi, S., Webster, M., Polimeni, J. R., van Essen, D., Jenkinson, M., & WU-Minn HCP Consortium. (2013). The minimal preprocessing pipelines for the human Connectome project. NeuroImage, 80, 105–124. https://doi.org/10.1016/j.neuroimage.2013.04.127.
Heckemann, R. A., Ledig, C., Gray, K. R., Aljabar, P., Rueckert, D., Hajnal, J. V., & Hammers, A. (2015). Brain extraction using label propagation and group agreement: Pincram. PLoS One, 10(7), e0129211. https://doi.org/10.1371/journal.pone.0129211.
Hoyer, C., Gass, N., Weber-Fahr, W., & Sartorius, A. (2014). Advantages and challenges of small animal magnetic resonance imaging as a translational tool. Neuropsychobiology, 69(4), 187–201. https://doi.org/10.1159/000360859.
Huang, A., Abugharbieh, R., Tam, R., & Traboulsee, A. (2006). Brain extraction using geodesic active contours. In J. M. Reinhardt & J. P. W. Pluim (Eds.), Proceedings of SPIE (Vol. 6144, p. 61444J). https://doi.org/10.1117/12.654160
Huang, A., Abugharbieh, R., Tam, R., & Traboulsee, A. (2007). MRI brain extraction with combined expectation maximization and geodesic active contours. Sixth IEEE International Symposium on Signal Processing and Information Technology, ISSPIT, 107(1), 107–111. https://doi.org/10.1109/ISSPIT.2006.270779.
Iglesias, J. E., Liu, C. Y., Thompson, P. M., & Tu, Z. (2011). Robust brain extraction across datasets and comparison with publicly available methods. IEEE Transactions on Medical Imaging, 30(9), 1617–1634. https://doi.org/10.1109/TMI.2011.2138152.
Johnson, G. A., Badea, A., Brandenburg, J., Cofer, G., Fubara, B., Liu, S., & Nissanov, J. (2010). Waxholm space: An image-based reference for coordinating mouse brain research. NeuroImage, 53(2), 365–372. https://doi.org/10.1016/j.neuroimage.2010.06.067.
Jonckers, E., Shah, D., Hamaide, J., Verhoye, M., & Van der Linden, A. (2015). The power of using functional fMRI on small rodents to study brain pharmacology and disease. Frontiers in Pharmacology, 6, 231. https://doi.org/10.3389/fphar.2015.00231.
Kleesiek, J., Urban, G., Hubert, A., Schwarz, D., Maier-Hein, K., Bendszus, M., & Biller, A. (2016). Deep MRI brain extraction: A 3D convolutional neural network for skull stripping. NeuroImage, 129, 460–469. https://doi.org/10.1016/j.neuroimage.2016.01.024.
Krukowski, K., Chou, A., Feng, X., Tiret, B., Paladini, M.-S., Riparip, L.-K., Chaumeil, M. M., Lemere, C., & Rosi, S. (2018). Traumatic brain injury in aged mice induces chronic microglia activation, synapse loss, and complement-dependent memory deficits. International Journal of Molecular Sciences, 19(12), 3753. https://doi.org/10.3390/ijms19123753.
Lee, J., Jomier, J., Aylward, S., Tyszka, M., Moy, S., Lauder, J., & Styner, M. (2009). Evaluation of atlas based mouse brain segmentation. In J. P. W. Pluim & B. M. Dawant (Eds.), Proc. of SPIE (Vol. 7259, p. 725943). NIH Public Access. https://doi.org/10.1117/12.812762.
Leung, K. K., Barnes, J., Modat, M., Ridgway, G. R., Bartlett, J. W., Fox, N. C., & Ourselin, S. (2011). Brain MAPS: An automated, accurate and robust brain extraction technique using a template library. NeuroImage, 55(3), 1091–1108. https://doi.org/10.1016/j.neuroimage.2010.12.067.
Li, J., Liu, X., Zhuo, J., Gullapalli, R. P., & Zara, J. M. (2013). An automatic rat brain extraction method based on a deformable surface model. Journal of Neuroscience Methods, 218(1), 72–82. https://doi.org/10.1016/j.jneumeth.2013.04.011.
Liang, Z., King, J., & Zhang, N. (2011). Uncovering intrinsic connectional architecture of functional networks in awake rat brain. The Journal of Neuroscience, 31(10), 3776–3783. https://doi.org/10.1523/JNEUROSCI.4557-10.2011.
Liang, Z., King, J., & Zhang, N. (2014). Neuroplasticity to a single-episode traumatic stress revealed by resting-state fMRI in awake rats. NeuroImage, 103, 485–491. https://doi.org/10.1016/j.neuroimage.2014.08.050.
Liang, Z., Watson, G. D. R., Alloway, K. D., Lee, G., Neuberger, T., & Zhang, N. (2015). Mapping the functional network of medial prefrontal cortex by combining optogenetics and fMRI in awake rats. NeuroImage, 117, 114–123. https://doi.org/10.1016/j.neuroimage.2015.05.036.
Liang, Z., Ma, Y., Watson, G. D. R., & Zhang, N. (2017). Simultaneous GCaMP6-based fiber photometry and fMRI in rats. Journal of Neuroscience Methods, 289, 31–38. https://doi.org/10.1016/j.jneumeth.2017.07.002.
Liu, Y., Perez, P. D., Ma, Z., Ma, Z., Dopfel, D., Cramer, S., et al. (2019). An open database of resting-state fMRI in awake rats. bioRxiv. https://doi.org/10.1101/842807.
Ma, Y. (2008). In vivo 3D digital atlas database of the adult C57BL/6J mouse brain by magnetic resonance microscopy. Frontiers in Neuroanatomy, 2(April), 1–10. https://doi.org/10.3389/neuro.05.001.2008.
Ma, Y., Hof, P. R., Grant, S. C., Blackband, S. J., Bennett, R., Slatest, L., et al. (2005). A three-dimensional digital atlas database of the adult C57BL/6J mouse brain by magnetic resonance microscopy. Neuroscience, 135(4), 1203–1215. https://doi.org/10.1016/j.neuroscience.2005.07.014.
Matas, J., Chum, O., Urban, M., & Pajdla, T. (2004). Robust wide-baseline stereo from maximally stable extremal regions. Image and Vision Computing, 22(10), 761–767. https://doi.org/10.1016/j.imavis.2004.02.006.
Murugavel, M., & Sullivan, J. M. (2009). Automatic cropping of MRI rat brain volumes using pulse coupled neural networks. NeuroImage, 45(3), 845–854. https://doi.org/10.1016/j.neuroimage.2008.12.021.
Oguz, I., Lee, J., Budin, F., Rumple, A., McMurray, M., Ehlers, C., et al. (2011). Automatic skull-stripping of rat MRI/DTI scans and atlas building. In B. M. Dawant & D. R. Haynor (Eds.), Proceedings of SPIE--the International Society for Optical Engineering (Vol. 7962, p. 796225). https://doi.org/10.1117/12.878405.
Oguz, I., Zhang, H., Rumple, A., & Sonka, M. (2014). RATS: Rapid automatic tissue segmentation in rodent brain MRI. Journal of Neuroscience Methods, 221, 175–182. https://doi.org/10.1016/j.jneumeth.2013.09.021.
Papp, E. A., Leergaard, T. B., Calabrese, E., Johnson, G. A., & Bjaalie, J. G. (2014). Waxholm space atlas of the Sprague Dawley rat brain. NeuroImage, 97, 374–386. https://doi.org/10.1016/j.neuroimage.2014.04.001.
Roy, S., Knutsen, A., Korotcov, A., Bosomtwi, A., Dardzinski, B., Butman, J. A., & Pham, D. L. (2018). A deep learning framework for brain extraction in humans and animals with traumatic brain injury. In 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018) (pp. 687–691). IEEE. https://doi.org/10.1109/ISBI.2018.8363667.
Shattuck, D. W., & Leahy, R. M. (2002). BrainSuite: An automated cortical surface identification tool. Medical Image Analysis, 6(2), 129–142. https://doi.org/10.1016/S1361-8415(02)00054-3.
Shattuck, D. W., Sandor-Leahy, S. R., Schaper, K. A., Rottenberg, D. A., & Leahy, R. M. (2001). Magnetic resonance image tissue classification using a partial volume model. NeuroImage, 13(5), 856–876. https://doi.org/10.1006/nimg.2000.0730.
Sled, J. G., Zijdenbos, A. P., & Evans, A. C. (1998). A nonparametric method for automatic correction of intensity nonuniformity in MRI data. IEEE Transactions on Medical Imaging, 17(1), 87–97. https://doi.org/10.1109/42.668698.
Smith, S. M. (2002). Fast robust automated brain extraction. Human Brain Mapping, 17(3), 143–155. https://doi.org/10.1002/hbm.10062.
Sonka, M., Hlavac, V., & Boyle, R. (2007). Image processing, analysis, and machine vision. Thomson-Engineering.
Totenhagen, J. W., Bernstein, A., Yoshimaru, E. S., Erickson, R. P., & Trouard, T. P. (2017). Quantitative magnetic resonance imaging of brain atrophy in a mouse model of Niemann-pick type C disease. PLoS One, 12(5), 1–11. https://doi.org/10.1371/journal.pone.0178179.
Vedaldi, A., & Fulkerson, B. (2010). VLFeat - an open and portable library of computer vision algorithms. Design, 3(1), 1–4. https://doi.org/10.1145/1873951.1874249.
Wood, T. C., Lythgoe, D. J., & Williams, S. C. R. (2013). rBET: Making BET work for rodent brains. In Proc Intl Soc Mag Reson Med, 21 (2013) (p. 2707).
Zhang, S., Huang, J., Uzunbas, M., Shen, T., & Delis, F. (2011). 3D segmentation of rodent brain structures using active volume model with shape priors. In Med Image Comput Comput Assist Interv (pp. 433-436). https://doi.org/10.1109/ISBI.2011.5872439
Zheng, S., Bai, Y.-Y., Liu, Y., Gao, X., Li, Y., Changyi, Y., Wang, Y., Chang, D., Ju, S., & Li, C. (2015). Salvaging brain ischemia by increasing neuroprotectant uptake via nanoagonist mediated blood brain barrier permeability enhancement. Biomaterials, 66, 9–20. https://doi.org/10.1016/j.biomaterials.2015.07.006.
Acknowledgments
The present study was partially supported by National Institute of Neurological Disorders and Stroke (R01NS085200, PI: Nanyin Zhang, PhD) and National Institute of Mental Health (R01MH098003 and RF1MH114224, PI: Nanyin Zhang, PhD).
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Electronic supplementary material
ESM 1
(DOCX 158 kb)
Rights and permissions
About this article
Cite this article
Liu, Y., Unsal, H.S., Tao, Y. et al. Automatic Brain Extraction for Rodent MRI Images. Neuroinform 18, 395–406 (2020). https://doi.org/10.1007/s12021-020-09453-z
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12021-020-09453-z