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Neuroinformatics
Published in: Neuroinformatics 3/2021

02-01-2021 | Original Article

Generating Longitudinal Atrophy Evaluation Datasets on Brain Magnetic Resonance Images Using Convolutional Neural Networks and Segmentation Priors

Authors: Jose Bernal, Sergi Valverde, Kaisar Kushibar, Mariano Cabezas, Arnau Oliver, Xavier Lladó, The Alzheimer’s Disease Neuroimaging Initiative

Published in: Neuroinformatics | Issue 3/2021

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Abstract

Brain atrophy quantification plays a fundamental role in neuroinformatics since it permits studying brain development and neurological disorders. However, the lack of a ground truth prevents testing the accuracy of longitudinal atrophy quantification methods. We propose a deep learning framework to generate longitudinal datasets by deforming T1-w brain magnetic resonance imaging scans as requested through segmentation maps. Our proposal incorporates a cascaded multi-path U-Net optimised with a multi-objective loss which allows its paths to generate different brain regions accurately. We provided our model with baseline scans and real follow-up segmentation maps from two longitudinal datasets, ADNI and OASIS, and observed that our framework could produce synthetic follow-up scans that matched the real ones (Total scans= 584; Median absolute error: 0.03 ± 0.02; Structural similarity index: 0.98 ± 0.02; Dice similarity coefficient: 0.95 ± 0.02; Percentage of brain volume change: 0.24 ± 0.16; Jacobian integration: 1.13 ± 0.05). Compared to two relevant works generating brain lesions using U-Nets and conditional generative adversarial networks (CGAN), our proposal outperformed them significantly in most cases (p < 0.01), except in the delineation of brain edges where the CGAN took the lead (Jacobian integration: Ours - 1.13 ± 0.05 vs CGAN - 1.00 ± 0.02; p < 0.01). We examined whether changes induced with our framework were detected by FAST, SPM, SIENA, SIENAX, and the Jacobian integration method. We observed that induced and detected changes were highly correlated (Adj. R2 > 0.86). Our preliminary results on harmonised datasets showed the potential of our framework to be applied to various data collections without further adjustment.
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Appendix
Available only for authorised users
Footnotes
7
adni.​loni.​usc.​edu The ADNI was launched in 2003 as a public-private partnership, led by Principal Investigator Michael W. Weiner, MD. The primary goal of ADNI has been to test whether serial magnetic resonance imaging (MRI), positron emission tomography (PET), other biological markers, and clinical and neuropsychological assessment can be combined to measure the progression of mild cognitive impairment (MCI) and early Alzheimer’s disease (AD). For up-to-date information, see www.​adni-info.​org.
 
Literature
Amiri, H., de Sitter, A., Bendfeldt, K., Battaglini, M., Wheeler-Kingshott, C.A.G., Calabrese, M., Geurts, J.J., Rocca, M.A., Sastre-Garriga, J., Enzinger, C., & et al. (2018). Urgent challenges in quantification and interpretation of brain grey matter atrophy in individual MS patients using MRI. NeuroImage: Clinical, 19, 466–475.CrossRef
Andersson, J.L., Jenkinson, M., Smith, S., & et al. (2007). Non-linear registration aka Spatial normalisation FMRIB Technial Report TR07JA2. FMRIB Analysis Group of the University of Oxford.
Battaglini, M., Jenkinson, M., De Stefano, N., & Initiative, A.D.N. (2018). SIENA-XL for improving the assessment of gray and white matter volume changes on brain MRI. Human Brain Mapping, 39(3), 1063–1077.PubMedCrossRef
Bernal, J., Kushibar, K., Asfaw, D.S., Valverde, S., Oliver, A., Martí, R., & Lladó, X. (2019a). Deep convolutional neural networks for brain image analysis on magnetic resonance imaging: a review. Artificial Intelligence in Medicine, 95, 64–81.PubMedCrossRef
Bernal, J., Kushibar, K., Cabezas, M., Valverde, S., Oliver, A., & Lladó, X. (2019b). Quantitative analysis of patch-based fully convolutional neural networks for tissue segmentation on brain magnetic resonance imaging. IEEE Access, 7, 89986–90002.CrossRef
Brosch, T., Tang, L.Y., Yoo, Y., Li, D.K., Traboulsee, A., & Tam, R. (2016). Deep 3D convolutional encoder networks with shortcuts for multiscale feature integration applied to multiple sclerosis lesion segmentation. IEEE Transactions on Medical Imaging, 35(5), 1229–1239.PubMedCrossRef
Chartsias, A., Joyce, T., Giuffrida, M.V., & Tsaftaris, S.A. (2018). Multimodal MR synthesis via modality-invariant latent representation. IEEE Transactions on Medical Imaging, 37(3), 803–814.PubMedCrossRef
Çiçek, Ö., Abdulkadir, A., Lienkamp, S.S., Brox, T., & Ronneberger, O. (2016). 3D U-Net: learning dense volumetric segmentation from sparse annotation. In International conference on medical image computing and computer-assisted intervention (pp. 424–432): Springer.
Costa, P., Galdran, A., Meyer, M.I., Niemeijer, M., Abràmoff, M., Mendonċa, A.M., & Campilho, A. (2018). End-to-end adversarial retinal image synthesis. IEEE Transactions on Medical Imaging, 37 (3), 781–791.PubMedCrossRef
Cover, K.S., van Schijndel, R.A., van Dijk, B.W., Redolfi, A., Knol, D.L., Frisoni, G.B., Barkhof, F., Vrenken, H., Initiative, A.D.N., & et al. (2011). Assessing the reproducibility of the SIENAX and SIENA brain atrophy measures using the ADNI back-to-back MP-RAGE MRI scans. Psychiatry Research: Neuroimaging, 193(3), 182–190.PubMedCrossRef
Crum, W.R., Camara, O., & Hill, D.L.G. (2006). Generalized overlap measures for evaluation and validation in medical image analysis. IEEE Transactions on Medical Imaging, 25(11), 1451– 1461.PubMedCrossRef
de Boer, R., Vrooman, H.A., Ikram, M.A., Vernooij, M.W., Breteler, M.M., van der Lugt, A., & Niessen, W.J. (2010). Accuracy and reproducibility study of automatic MRI brain tissue segmentation methods. NeuroImage, 51(3), 1047–1056.PubMedCrossRef
Dice, L.R. (1945). Measures of the amount of ecologic association between species. Ecology, 26 (3), 297–302.CrossRef
Ens, K., Wenzel, F., Young, S., Modersitzki, J., & Fischer, B. (2009). Design of a synthetic database for the validation of non-linear registration and segmentation of magnetic resonance brain images. In Medical imaging 2009: image processing, (Vol. 7259 p. 725933). International Society for Optics and Photonics.
Filippi, M., Rocca, M.A., Ciccarelli, O., De Stefano, N., Evangelou, N., Kappos, L., Rovira, A., Sastre-Garriga, J., Tintorè, M., Frederiksen, J.L., Gasperini, C., Palace, J., Reich, D.S., Banwell, B., Montalban, X., & Barkhof, F. (2016). MRI criteria for the diagnosis of multiple sclerosis: MAGNIMS consensus guidelines. The Lancet Neurology, 15(3), 292–303.PubMedPubMedCentralCrossRef
Fischl, B., Salat, D.H., Busa, E., Albert, M., Dieterich, M., Haselgrove, C., Van Der Kouwe, A., Killiany, R., Kennedy, D., Klaveness, S., & et al. (2002). Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain. Neuron, 33(3), 341–355.PubMedCrossRef
Fox, N.C., Jenkins, R., Leary, S.M., Stevenson, V.L., Losseff, N.A., Crum, W.R., Harvey, R.J., Rossor, M.N., Miller, D.H., & Thompson, A.J. (2000). Progressive cerebral atrophy in MS: a serial study using registered, volumetric MRI. Neurology, 54(4), 807–812.PubMedCrossRef
Freeborough, P.A., & Fox, N.C. (1997). The boundary shift integral: an accurate and robust measure of cerebral volume changes from registered repeat MRI. IEEE Transactions on Medical Imaging, 16(5), 623–629.PubMedCrossRef
Frid-Adar, M., Diamant, I., Klang, E., Amitai, M., Goldberger, J., & Greenspan, H. (2018). GAN-based synthetic medical image augmentation for increased CNN performance in liver lesion classification. Neurocomputing, 321, 321–331.CrossRef
Ghafoorian, M., Karssemeijer, N., Heskes, T., Uden, I.W., Sanchez, C.I., Litjens, G., Leeuw, F.E., Ginneken, B., Marchiori, E., & Platel, B. (2017). Location sensitive deep convolutional neural networks for segmentation of white matter hyperintensities. Scientific Reports, 7(1), 5110.PubMedPubMedCentralCrossRef
Glorot, X., Bordes, A., & Bengio, Y. (2011). Deep sparse rectifier neural networks. In Proceedings of the 14th international conference on artificial intelligence and statistics (pp. 315–323).
Guerrero, R., Qin, C., Oktay, O., Bowles, C., Chen, L., Joules, R., Wolz, R., Valdés-Hernández, M., Dickie, D., Wardlaw, J., & et al. (2018). White matter hyperintensity and stroke lesion segmentation and differentiation using convolutional neural networks. NeuroImage: Clinical, 17, 918–934.CrossRef
Haijma, S.V., Van Haren, N., Cahn, W., Koolschijn, P.C.M., Hulshoff Pol, H.E., & Kahn, R.S. (2012). Brain volumes in schizophrenia: a meta-analysis in over 18 000 subjects. Schizophrenia Bulletin, 39(5), 1129–1138.PubMedPubMedCentralCrossRef
He, K., Zhang, X., Ren, S., & Sun, J. (2015). Delving deep into rectifiers: surpassing human-level performance on imagenet classification. In Proceedings of the IEEE international conference on computer vision (pp. 1026–1034).
He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770–778).
Heimann, T., Van Ginneken, B., Styner, M.A., Arzhaeva, Y., Aurich, V., Bauer, C., Beck, A., Becker, C., Beichel, R., Bekes, G., & et al. (2009). Comparison and evaluation of methods for liver segmentation from CT datasets. IEEE Transactions on Medical Imaging, 28(8), 1251–1265.PubMedCrossRef
Hore, A., & Ziou, D. (2010). Image quality metrics: PSNR vs. SSIM. In Proceedings of the 20th international conference on pattern recognition (pp. 2366–2369).
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.PubMedCrossRef
Isola, P., Zhu, J.Y., Zhou, T., & Efros, A.A. (2017). Image-to-image translation with conditional adversarial networks. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 1125–1134).
Jenkinson, M., & Smith, S. (2001). A global optimisation method for robust affine registration of brain images. Medical Image Analysis, 5(2), 143–156.PubMedCrossRef
Jenkinson, M., Bannister, P., Brady, M., & Smith, S. (2002). Improved optimization for the robust and accurate linear registration and motion correction of brain images. NeuroImage, 17(2), 825–841.PubMedCrossRef
Jia, G., Heymsfield, S.B., Zhou, J., Yang, G., & Takayama, Y. (2016). Quantitative biomedical imaging: techniques and clinical applications. BioMed Research International.
Karaçali, B., & Davatzikos, C. (2006). Simulation of tissue atrophy using a topology preserving transformation model. IEEE Transactions on Medical Imaging, 25(5), 649–652.PubMedCrossRef
Khanal, B., Ayache, N., & Pennec, X. (2017). Simulating longitudinal brain mris with known volume changes and realistic variations in image intensity. Frontiers in Neuroscience, 11, 132.PubMedPubMedCentralCrossRef
Kingma, D.P., & Ba, J. (2014). Adam: a method for stochastic optimization. coRR arXiv:1409.​1556.
Krebs, J., e Delingette, H., Mailhé, B., Ayache, N., & Mansi, T. (2019). Learning a probabilistic model for diffeomorphic registration. IEEE Transactions on Medical Imaging, 38(9), 2165–2176.PubMedCrossRef
Li, G. (1985). Robust regression. Exploring Data Tables, Trends, and Shapes, 281, U340.
Marcus, D.S., Fotenos, A.F., Csernansky, J.G., Morris, J.C., & Buckner, R.L. (2010). Open access series of imaging studies: longitudinal MRI data in nondemented and demented older adults. Journal of Cognitive Neuroscience, 22(12), 2677–2684.PubMedPubMedCentralCrossRef
Nakamura, K., Guizard, N., Fonov, V.S., Narayanan, S., Collins, D.L., & Arnold, D.L. (2014). Jacobian integration method increases the statistical power to measure gray matter atrophy in multiple sclerosis. NeuroImage: Clinical, 4, 10–17.CrossRef
Nakamura, K., Eskildsen, S.F., Narayanan, S., Arnold, D.L., Collins, D.L., Initiative, A.D.N., & et al. (2018). Improving the SIENA performance using BEaST brain extraction. PloS One, 13(9), e0196945.PubMedPubMedCentralCrossRef
Nyúl, L.G., Udupa, J.K., & Zhang, X. (2000). New variants of a method of MRI scale standardization. IEEE Transactions on Medical Imaging, 19(2), 143–150.PubMedCrossRef
Patenaude, B., Smith, S.M., Kennedy, D.N., & Jenkinson, M. (2011). A Bayesian model of shape and appearance for subcortical brain segmentation. NeuroImage, 56(3), 907–922.PubMedCrossRef
Rocca, M.A., Battaglini, M., Benedict, R.H., De Stefano, N., Geurts, J.J., Henry, R.G., Horsfield, M.A., Jenkinson, M., Pagani, E., & Filippi, M. (2017). Brain MRI atrophy quantification in MS: from methods to clinical application. Neurology, 88(4), 403–413.PubMedPubMedCentralCrossRef
Rovira, À., Wattjes, M.P., Tintoré, M., Tur, C., Yousry, T.A., Sormani, M.P., De Stefano, N., Filippi, M., Auger, C., Rocca, M.A., & et al. (2015). Evidence-based guidelines: MAGNIMS consensus guidelines on the use of MRI in multiple sclerosis—clinical implementation in the diagnostic process. Nature Reviews Neurology, 11(8), 471–482.PubMedCrossRef
Roy, S., Carass, A., & Prince, J. (2013). Magnetic resonance image example-based contrast synthesis. IEEE Transactions on Medical Imaging, 32(12), 2348–2363.PubMedPubMedCentralCrossRef
Rudick, R.A., Fisher, E., Lee, J-C, Simon, J., Jacobs, L., Multiple Sclerosis Collaborative Research Group, & et al. (1999). Use of the brain parenchymal fraction to measure whole brain atrophy in relapsing-remitting MS. Neurology, 53(8), 1698–1698.PubMedCrossRef
Salem, M., Valverde, S., Cabezas, M., Pareto, D., Oliver, A., Salvi, J., Rovira À., & Lladó, X. (2019). Multiple Sclerosis Lesion Synthesis in MRI using an encoder-decoder U-NET. IEEE Access.
Sharma, S., Rousseau, F., Heitz, F., Rumbach, L., & Armspach, J.P. (2013). On the estimation and correction of bias in local atrophy estimations using example atrophy simulations. Computerized Medical Imaging and Graphics, 37(7–8), 538–551.PubMedCrossRef
Shin, H.C., Tenenholtz, N.A., Rogers, J.K., Schwarz, C.G., Senjem, M.L., Gunter, J.L., Andriole, K., & Michalski, M. (2018). Medical image synthesis for data augmentation and anonymization using generative adversarial networks. In International workshop on simulation and synthesis in medical imaging (pp. 1–11): Springer.
Smith, S.M., Zhang, Y., Jenkinson, M., Chen, J., Matthews, P., Federico, A., & De Stefano, N. (2002). Accurate, robust, and automated longitudinal and cross-sectional brain change analysis. NeuroImage, 17(1), 479–489.PubMedCrossRef
Springenberg, J.T., Dosovitskiy, A., Brox, T., & Riedmiller, M. (2015). Striving for simplicity: the all convolutional net. In ICLR (workshop track) (pp. 1–14).
Steenwijk, M.D., Geurts, J.J.G., Daams, M., Tijms, B.M., Wink, A.M., Balk, L.J., Tewarie, P.K., Uitdehaag, B.M.J., Barkhof, F., Vrenken, H., & et al. (2016). Cortical atrophy patterns in multiple sclerosis are non-random and clinically relevant. Brain, 139(1), 115–126.PubMedCrossRef
Storelli, L., Rocca, M.A., Pagani, E., Van Hecke, W., Horsfield, M.A., De Stefano, N., Rovira, A., Sastre-Garriga, J., Palace, J., Sima, D., & et al. (2018). Measurement of whole-brain and gray matter atrophy in multiple sclerosis: assessment with MR imaging. Radiology, 288(2), 554–564.PubMedCrossRef
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A., & et al. (2015). Going deeper with convolutions. In IEEE conference on computer vision and pattern recognition.
Szegedy, C., Ioffe, S., Vanhoucke, V., & Alemi, A.A. (2017). Inception-v4, inception-resnet and the impact of residual connections on learning. In AAAI (Vol. 4 p. 12).
Trottier, L., Gigu, P., Chaib-draa, B., & et al. (2017). Parametric exponential linear unit for deep convolutional neural networks. In 16th IEEE international conference on machine learning and applications (pp. 207–214).
van Erp, T.G., Hibar, D.P., Rasmussen, J.M., Glahn, D.C., Pearlson, G.D., Andreassen, O.A., Agartz, I., Westlye, L.T., Haukvik, U.K., Dale, A.M., & et al. (2016). Subcortical brain volume abnormalities in 2028 individuals with schizophrenia and 2540 healthy controls via the ENIGMA consortium. Molecular Psychiatry, 21(4), 547.PubMedCrossRef
Wang, Z., Bovik, A.C., Sheikh, H.R., & Simoncelli, E.P. (2004). Image quality assessment: from error visibility to structural similarity. IEEE Transactions on Image Processing, 13(4), 600–612.PubMedCrossRef
Wei, W., Poirion, E., Bodini, B., Durrleman, S., Colliot, O., Stankoff, B., & Ayache, N. (2018). FLAIR MR image synthesis by using 3D fully convolutional networks for multiple sclerosis. In ISMRM-ESMRMB 2018-joint annual meeting (pp. 1–6).
Zhang, Y., Brady, M., & Smith, S. (2001). Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm. IEEE Transactions on Medical Imaging, 20 (1), 45–57.PubMedCrossRef
Metadata
Title
Generating Longitudinal Atrophy Evaluation Datasets on Brain Magnetic Resonance Images Using Convolutional Neural Networks and Segmentation Priors
Authors
Jose Bernal
Sergi Valverde
Kaisar Kushibar
Mariano Cabezas
Arnau Oliver
Xavier Lladó
The Alzheimer’s Disease Neuroimaging Initiative
Publication date
02-01-2021
Publisher
Springer US
Published in
Neuroinformatics / Issue 3/2021
Print ISSN: 1539-2791
Electronic ISSN: 1559-0089
DOI
https://doi.org/10.1007/s12021-020-09499-z

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