Abstract
Purpose
Structural magnetic resonance imaging (MRI) data is essential for many neuroscience and clinical applications. The morphological features presented in the human brain are a rich source of information for understanding healthy development and pathological status, assisting the evaluation of brain atrophy, cortical thickness, and healthy brain aging. Several efforts have been made by the scientific community to offer robust automatic segmentation methods to brain analysis, helping health professionals in such complicated and time-consuming tasks. However, the precise definition of brain tissues is not trivial and still is an open problem, being an important topic for modern research.
Methods
We presented an improved brain segmentation algorithm in this study, which is focused to robustly detect the main brain tissues, i.e., CSF, GM, and WM. We propose the Brain Logistic Segmentation (BLS) method based on multiple logistic classification algorithms, which was evaluated with simulated (BrainWeb) and real MRI image data sets (IBSR, OASIS). The brain segmentation quality was measured with Dice (DICE), Volume Similarity (VOLSIM), the 95th percentile of Hausdorff Distance (HDIST), normalized Whole-Brain Volume Absolute Error (nWBV-AE), and compared speeds with total processing time (t).
Results
We compared the proposed method (BLS) to the most well-known brain tissue segmentation algorithms, i.e., FSL-FAST, Atropos, SPM12, and BrainSuite-PVC. The results show a significant (p < 0.01, paired t test) improvement in segmentation with the BLS method, evidencing a better local tissue definition with the BLS.
Conclusion
The BLS method showed significant improvements to be eligible for brain analysis in neuroscience and clinical applications such as brain atrophy and cortical thickness measurement.
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Data Availability
All the data used in this study were obtained in public research repositories, which were properly indicated in Methods section.
Notes
Available from the Center for Morphometric Analysis at Massachusetts General Hospital.
References
Arnold JB, Liow J-S, Schaper KA, Stern JJ, Sled JG, Shattuck DW, Worth AJ, Cohen MS, Leahy RM, Mazziotta JC, Rottenberg DA. Qualitative and quantitative evaluation of six algorithms for correcting intensity nonuniformity effects. Neuroimage. 2001;13(5):931–43. https://doi.org/10.1006/nimg.2001.0756.
Ashburner J, Friston KJ. Unified segmentation. Neuroimage. 2005;26(3):839–51. https://doi.org/10.1016/j.neuroimage.2005.02.018.
Avants BB, Tustison NJ, Wu J, Cook PA, Gee JC. An open source multivariate framework for N-tissue segmentation with evaluation on public data. Neuroinformatics. 2011;9(4):381–400. https://doi.org/10.1007/s12021-011-9109-y.
Azevedo CJ, Cen SY, Jaberzadeh A, Zheng L, Hauser SL, Pelletier D. Contribution of normal aging to brain atrophy in MS. Neurol - Neuroimmunol Neuroinflammation. 2019;6(6):e616. https://doi.org/10.1212/NXI.0000000000000616.
Balafar MA, Ramli AR, Saripan MI, Mashohor S. Review of brain MRI image segmentation methods. Artif Intell Rev. 2010;33(3):261–74. https://doi.org/10.1007/s10462-010-9155-0.
Bashyam V. M., Erus, G., Doshi, J., Habes, M., Nasrallah, I. M., Truelove-Hill, M., Srinivasan, D., Mamourian, L., Pomponio, R., Fan, Y., Launer, L. J., Masters, C. L., Maruff, P., Zhuo, C., Völzke, H., Johnson, S. C., Fripp, J., Koutsouleris, N., Satterthwaite, T. D., ... Davatzikos, C. (2020). MRI signatures of brain age and disease over the lifespan based on a deep brain network and 14 468 individuals worldwide. Brain, 143(7), 2312–2324. https://doi.org/10.1093/brain/awaa160
Bermel RA, Bakshi R. The measurement and clinical relevance of brain atrophy in multiple sclerosis. Lancet Neurol. 2006;5(2):158–70. https://doi.org/10.1016/S1474-4422(06)70349-0.
Bernasconi, A., Cendes, F., Theodore, W. H., Gill, R. S., Koepp, M. J., Hogan, R. E., Jackson, G. D., Federico, P., Labate, A., Vaudano, A. E., Blümcke, I., Ryvlin, P., & Bernasconi, N. (2019). Recommendations for the use of structural magnetic resonance imaging in the care of patients with epilepsy: a consensus report from the International League Against Epilepsy Neuroimaging Task Force. Epilepsia, epi.15612. https://doi.org/10.1111/epi.15612
Buckner RL, Head D, Parker J, Fotenos AF, Marcus D, Morris JC, Snyder AZ. A unified approach for morphometric and functional data analysis in young, old, and demented adults using automated atlas-based head size normalization: reliability and validation against manual measurement of total intracranial volume. Neuroimage. 2004;23(2):724–38. https://doi.org/10.1016/j.neuroimage.2004.06.018.
Cárdenas-Blanco A, Tejos C, Irarrazaval P, Cameron I. Noise in magnitude magnetic resonance images. Concepts Magn Reson. 2008;32A(6):409–16. https://doi.org/10.1002/cmr.a.20124.
Cardenes R, de Luis-Garcia R, Bach-Cuadra M. A multidimensional segmentation evaluation for medical image data. Comput Methods Programs Biomed. 2009;96(2):108–24. https://doi.org/10.1016/j.cmpb.2009.04.009.
Chu R, Kim G, Tauhid S, Khalid F, Healy BC, Bakshi R. Whole brain and deep gray matter atrophy detection over 5 years with 3T MRI in multiple sclerosis using a variety of automated segmentation pipelines. PLoS ONE. 2018;13(11):e0206939. https://doi.org/10.1371/journal.pone.0206939.
Cocosco CA, Kollokian V, Kwan RKS, Evans AC. BrainWeb: online interface to a 3D MRI simulated brain database. NeuroImage. 1997;5(4):S425. http://www.bic.mni.mcgill.ca/brainweb/.
Daffner, K. R. (2010). Promoting successful cognitive aging: a comprehensive review. In Journal of Alzheimer’s Disease (Vol. 19, Issue 4, pp. 1101–1122). NIH Public Access. https://doi.org/10.3233/JAD-2010-1306
Desikan RS, Ségonne F, Fischl B, Quinn BT, Dickerson BC, Blacker D, Buckner RL, Dale AM, Maguire RP, Hyman BT, Albert MS, Killiany RJ. An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage. 2006;31(3):968–80. https://doi.org/10.1016/j.neuroimage.2006.01.021.
Despotović I, Goossens B, Philips W. MRI segmentation of the human brain: challenges, methods, and applications. Comput Math Methods Med. 2015;2015:1–23. https://doi.org/10.1155/2015/450341.
Dice LR. Measures of the amount of ecologic association between species. Ecology. 1945;26(3):297–302. https://doi.org/10.2307/1932409.
Dogdas B, Shattuck DW, Leahy RM. Segmentation of skull and scalp in 3-D human MRI using mathematical morphology. Hum Brain Mapp. 2005;26(4):273–85. https://doi.org/10.1002/hbm.20159.
Dora, L., Agrawal, S., Panda, R., & Abraham, A. (2017). State of the art methods for brain tissue segmentation: a review. IEEE Reviews in Biomedical Engineering, 1–1. https://doi.org/10.1109/RBME.2017.2715350
Fellhauer I, Zöllner FG, Schröder J, Degen C, Kong L, Essig M, Thomann PA, Schad LR. Comparison of automated brain segmentation using a brain phantom and patients with early Alzheimer’s dementia or mild cognitive impairment. Psychiatry Res: Neuroimaging. 2015;233(3):299–305. https://doi.org/10.1016/j.pscychresns.2015.07.011.
Fotenos AF, Snyder AZ, Girton LE, Morris JC, Buckner RL. Normative estimates of cross-sectional and longitudinal brain volume decline in aging and AD. Neurology. 2005;64(6):1032–9. https://doi.org/10.1212/01.WNL.0000154530.72969.11.
Fragoso YD, Willie PR, Goncalves MVM, Brooks JBB. Critical analysis on the present methods for brain volume measurements in multiple sclerosis. Arq Neuropsiquiatr. 2017;75(7):464–9. https://doi.org/10.1590/0004-282X20170072.
Friston KJ, Ashburner JT, Kiebel SJ, Nichols TE, Penny WD. Statistical parametric mapping: the analysis of functional brain images. Elsevier/Academic Press; 2007. https://doi.org/10.1016/B978-0-12-372560-8.X5000-1
Ghaffari M, Sowmya A, Oliver R. Automated brain tumor segmentation using multimodal brain scans: a survey based on models submitted to the BraTS 2012–2018 challenges. IEEE Rev Biomed Eng. 2020;13:156–68. https://doi.org/10.1109/RBME.2019.2946868.
Gilmore JH, Shi F, Woolson SL, Knickmeyer RC, Short SJ, Lin W, Zhu H, Hamer RM, Styner M, Shen D. Longitudinal development of cortical and subcortical gray matter from birth to 2 years. Cereb Cortex. 2012;22(11):2478–85. https://doi.org/10.1093/cercor/bhr327.
González-Villà S, Oliver A, Valverde S, Wang L, Zwiggelaar R, Lladó X. A review on brain structures segmentation in magnetic resonance imaging. Artif Intell Med. 2016;73:45–69. https://doi.org/10.1016/j.artmed.2016.09.001.
Gunning-Dixon, F. M., Brickman, A. M., Cheng, J. C., & Alexopoulos, G. S. (2009). Aging of cerebral white matter: a review of MRI findings. In International Journal of Geriatric Psychiatry (Vol. 24, Issue 2, pp. 109–117). NIH Public Access. https://doi.org/10.1002/gps.2087
Harada, C. N., Natelson Love, M. C., & Triebel, K. L. (2013). Normal cognitive aging. In Clinics in Geriatric Medicine (Vol. 29, Issue 4, pp. 737–752). NIH Public Access. https://doi.org/10.1016/j.cger.2013.07.002
Heinen R, Bouvy WH, Mendrik AM, Viergever MA, Biessels GJ, De Bresser J. Robustness of automated methods for brain volume measurements across different MRI field strengths. PLoS ONE. 2016;11(10):e0165719. https://doi.org/10.1371/journal.pone.0165719.
Jiang, H., Lu, N., Chen, K., Yao, L., Li, K., Zhang, J., & Guo, X. (2020). Predicting brain age of healthy adults based on structural MRI parcellation using convolutional neural networks. Frontiers in Neurology, 10. https://doi.org/10.3389/fneur.2019.01346
Jonsson BA, Bjornsdottir G, Thorgeirsson TE, Ellingsen LM, Walters GB, Gudbjartsson DF, Stefansson H, Stefansson K, Ulfarsson MO. Brain age prediction using deep learning uncovers associated sequence variants. Nat Commun. 2019;10(1):5409. https://doi.org/10.1038/s41467-019-13163-9.
Kazemi K, Noorizadeh N. Quantitative comparison of SPM, FSL, and Brainsuite for brain MR image segmentation. J Biomed Phys Eng. 2014;4:13–26.
Krüger MT, Kurtev-Rittstieg R, Kägi G, Naseri Y, Hägele-Link S, Brugger F. Evaluation of automatic segmentation of thalamic nuclei through clinical effects using directional deep brain stimulation leads: a technical note. Brain Sci. 2020;10(9):642. https://doi.org/10.3390/brainsci10090642.
Lenchik L, Heacock L, Weaver AA, Boutin RD, Cook TS, Itri J, Filippi CG, Gullapalli RP, Lee J, Zagurovskaya M, Retson T, Godwin K, Nicholson J, Narayana PA. Automated segmentation of tissues using CT and MRI: a systematic review. Acad Radiol. 2019;26(12):1695–706. https://doi.org/10.1016/j.acra.2019.07.006.
Li, W., Wang, G., Fidon, L., Ourselin, S., Cardoso, M. J., & Vercauteren, T. (2017). On the compactness, efficiency, and representation of 3D convolutional networks: brain parcellation as a pretext task (348–360). https://doi.org/10.1007/978-3-319-59050-9_28
Ma H, Sheng L, Chen F, Yuan C, Dai Z, Pan P. Cortical Thickness Chronic Pain Med. 2020;99(31):e21499. https://doi.org/10.1097/MD.0000000000021499.
Marcus DS, Wang TH, Parker J, Csernansky JG, Morris JC, Buckner RL. Open Access Series of Imaging Studies (OASIS): cross-sectional MRI data in young, middle aged, nondemented, and demented older adults. J Cogn Neurosci. 2007;19(9):1498–507. https://doi.org/10.1162/jocn.2007.19.9.1498.
Mendrik, A. M., Vincken, K. L., Kuijf, H. J., Breeuwer, M., Bouvy, W. H., De Bresser, J., Alansary, A., De Bruijne, M., Carass, A., El-Baz, A., Jog, A., Katyal, R., Khan, A. R., Van Der Lijn, F., Mahmood, Q., Mukherjee, R., Van Opbroek, A., Paneri, S., Pereira, S., ... Viergever, M. A. (2015). MRBrainS challenge: online evaluation framework for brain image segmentation in 3T MRI scans. Computational Intelligence and Neuroscience, 2015, 1–16. https://doi.org/10.1155/2015/813696
Pieper, S., Halle, M., & Kikinis, R. (2004). 3D Slicer. 2004 2nd IEEE International Symposium on Biomedical Imaging: macro to nano (IEEE Cat No. 04EX821), 2, 632–635. https://doi.org/10.1109/ISBI.2004.1398617
Pirko, I., Lucchinetti, C. F., Sriram, S., & Bakshi, R. (2007). Gray matter involvement in multiple sclerosis. In Neurology (Vol. 68, Issue 9, pp. 634–642). Lippincott Williams & Wilkins. https://doi.org/10.1212/01.wnl.0000250267.85698.7a
Righart R, Schmidt P, Dahnke R, Biberacher V, Beer A, Buck D, Hemmer B, Kirschke JS, Zimmer C, Gaser C, Mühlau M. Volume versus surface-based cortical thickness measurements: a comparative study with healthy controls and multiple sclerosis patients. PLoS ONE. 2017;12(7):e0179590. https://doi.org/10.1371/journal.pone.0179590.
Rocca MA, Battaglini M, Benedict RHB, De Stefano N, Geurts JJG, Henry RG, Horsfield MA, Jenkinson M, Pagani E, Filippi M. Brain MRI atrophy quantification in MS. Neurology. 2017;88(4):403–13. https://doi.org/10.1212/WNL.0000000000003542.
Sanjay-Gopal S, Hebert TJ. Bayesian pixel classification using spatially variant finite mixtures and the generalized EM algorithm. IEEE Trans Image Process. 1998;7(7):1014–28. https://doi.org/10.1109/83.701161.
Schwarz CG, Gunter JL, Wiste HJ, Przybelski SA, Weigand SD, Ward CP, Senjem ML, Vemuri P, Murray ME, Dickson DW, Parisi JE, Kantarci K, Weiner MW, Petersen RC, Jack CR, Alzheimer’s Disease Neuroimaging Initiative, & Initiative, A. D. N. A large-scale comparison of cortical thickness and volume methods for measuring Alzheimer’s disease severity. NeuroImage Clin. 2016;11:802–12. https://doi.org/10.1016/j.nicl.2016.05.017.
da Silva Senra Filho AC (2017). A hybrid approach based on logistic classification and iterative contrast enhancement algorithm for hyperintense multiple sclerosis lesion segmentation. Medical & Biological Engineering & Computinghttps://doi.org/10.1007/s11517-017-1747-2
Senra Filho, A. C. da S. (2019). An empirical optimization to logistic classification model. The Insight Journal. https://doi.org/10.54294/sqeqvp
Shattuck, D. W., & Leahy, R. M. (2002). BrainSuite: an automated cortical surface identification tool. Medical Image Analysis, 6(2), 129–142. http://www.ncbi.nlm.nih.gov/pubmed/12045000
Singh MK, Singh KK. A review of publicly available automatic brain segmentation methodologies, machine learning models, recent advancements, and their comparison. Ann Neurosci. 2021;28(1–2):82–93. https://doi.org/10.1177/0972753121990175.
Smeets D, Ribbens A, Sima DM, Cambron M, Horakova D, Jain S, Maertens A, Van Vlierberghe E, Terzopoulos V, Van Binst AM, Vaneckova M, Krasensky J, Uher T, Seidl Z, De Keyser J, Nagels G, De Mey J, Havrdova E, Van Hecke W. Reliable measurements of brain atrophy in individual patients with multiple sclerosis. Brain and Behavior. 2016;6(9):e00518. https://doi.org/10.1002/brb3.518.
Suh JS, Schneider MA, Minuzzi L, MacQueen GM, Strother SC, Kennedy SH, Frey BN. Cortical thickness in major depressive disorder: a systematic review and meta-analysis. Prog Neuropsychopharmacol Biol Psychiatry. 2019;88:287–302. https://doi.org/10.1016/j.pnpbp.2018.08.008.
Tabatabaei-Jafari H, Shaw ME, Cherbuin N. Cerebral atrophy in mild cognitive impairment: a systematic review with meta-analysis. Alzheimer’s Dementia: Diagn, Assessment Dis Monit. 2015;1(4):487–504. https://doi.org/10.1016/j.dadm.2015.11.002.
Taha AA, Hanbury A. An efficient algorithm for calculating the exact Hausdorff distance. IEEE Trans Pattern Anal Mach Intell. 2015;37(11):2153–63. https://doi.org/10.1109/TPAMI.2015.2408351.
Takao H, Abe O, Ohtomo K. Computational analysis of cerebral cortex. Neuroradiology. 2010;52(8):691–8. https://doi.org/10.1007/s00234-010-0715-4.
Tiwari A, Srivastava S, Pant M. Brain tumor segmentation and classification from magnetic resonance images: review of selected methods from 2014 to 2019. Pattern Recogn Lett. 2020;131:244–60. https://doi.org/10.1016/j.patrec.2019.11.020.
Tudorascu, D. L., Karim, H. T., Maronge, J. M., Alhilali, L., Fakhran, S., Aizenstein, H. J., Muschelli, J., & Crainiceanu, C. M. (2016). Reproducibility and bias in healthy brain segmentation: comparison of two popular neuroimaging platforms. Frontiers in Neuroscience, 10(NOV), 503. https://doi.org/10.3389/fnins.2016.00503
Valverde S, Oliver A, Cabezas M, Roura E, Lladó X. Comparison of 10 brain tissue segmentation methods using revisited IBSR annotations. J Magn Reson Imaging. 2015;41(1):93–101. https://doi.org/10.1002/jmri.24517.
Veloz A, Orellana A, Vielma J, Salas R, Chabert S. Brain tumors: how can images and segmentation techniques help? 2011. https://doi.org/10.5772/22466.
Young, G. S. (2007). Advanced MRI of adult brain tumors. Neurologic Clinics, 25(4), 947–973, viii. https://doi.org/10.1016/j.ncl.2007.07.010
Zhang Y, Brady M, Smith S. Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm. IEEE Trans Med Imaging. 2001;20(1):45–57. https://doi.org/10.1109/42.906424.
Zhou, X., Ye, Q., Jiang, Y., Wang, M., Niu, Z., Menpes-Smith, W., Fang, E. F., Liu, Z., Xia, J., & Yang, G. (2020). Systematic and comprehensive automated ventricle segmentation on ventricle images of the elderly patients: a retrospective study. Frontiers in Aging Neuroscience, 12. https://doi.org/10.3389/fnagi.2020.618538
Zivadinov R, Stosic M, Cox JL, Ramasamy DP, Dwyer MG. The place of conventional MRI and newly emerging MRI techniques in monitoring different aspects of treatment outcome. J Neurol. 2008;255(SUPPL. 1):61–74. https://doi.org/10.1007/s00415-008-1009-1.
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The authors would like to thank the financial support provided by Conselho Nacional de Pesquisa (CNPq), grant number 405574/2017-7.
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Senra Filho, A.C.d.S., Junior, L.O.M. Brain Logistic Segmentation (BLS): an efficient algorithm for whole-brain tissue segmentation in structural magnetic resonance imaging. Res. Biomed. Eng. 40, 1–13 (2024). https://doi.org/10.1007/s42600-023-00325-4
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DOI: https://doi.org/10.1007/s42600-023-00325-4