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International Workshop on Machine Learning in Medical Imaging

MLMI 2020: Machine Learning in Medical Imaging pp 353–362Cite as

Learning Conditional Deformable Shape Templates for Brain Anatomy

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12436))

Abstract

A brain template that describes the anatomical layout of an “average” brain is an essential building block of neuroimage analysis pipelines. However, a single template is often not sufficient to fully capture the variability in a heterogeneous population. Brain structures have very different shapes and sizes in different clinical and demographic groups. In this paper, we develop a novel neural network model that captures this morphometric variability. Our model learns to compute an attribute-specific spatial deformation that warps a brain template. We train this model on individual brain MRI segmentations in an end-to-end fashion, allowing for fast inference during testing. We demonstrate the ability of our model to deform a brain template given a wide range of ages, presence of disease and different sexes. Detailed qualitative and quantitative experiments are provided in order to demonstrate the flexibility of our model. Finally, we study the surface of the deformed template’s hippocampus to show how our model can be used for shape analysis. The code is freely available at https://github.com/evanmy/conditional_deformation.

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Notes

  1. 1.

    https://www.oasis-brains.org/.

  2. 2.

    Divided by the total WM/GM volume of a 20 year old male or female.

References

  1. Passe, T.J., et al.: Age and sex effects on brain morphology. Prog. Neuro-psychopharmacol. Biol. Psychiatry 21, 1231–1237 (1997)

    Article  Google Scholar 

  2. Raz, N., et al.: Aging, sexual dimorphism, and hemispheric asymmetry of the cerebral cortex: replicability of regional differences in volume. Neurobiol. Aging 25(3), 377–396 (2004)

    Article  Google Scholar 

  3. Raz, N., et al.: Regional brain changes in aging healthy adults: general trends, individual differences and modifiers. Cereb. Cortex 15(11), 1676–1689 (2005)

    Article  Google Scholar 

  4. Hedden, T., Gabrieli, J.D.E.: Insights into the ageing mind: a view from cognitive neuroscience. Nat. Rev. Neurosci. 5(2), 87–96 (2004)

    Article  Google Scholar 

  5. Fotenos, A.F., et al.: Normative estimates of cross-sectional and longitudinal brain volume decline in aging and AD. Neurology 64(6), 1032–1039 (2005)

    Article  Google Scholar 

  6. Serrano-Pozo, A., et al.: Neuropathological alterations in Alzheimer disease. Cold Spring Harbor Perspect. Med. 1(1), a006189 (2011)

    Article  Google Scholar 

  7. Vita, A., et al.: Brain morphology in first-episode schizophrenia: a meta-analysis of quantitative magnetic resonance imaging studies. Schizophr. Res. 82(1), 75–88 (2006)

    Article  Google Scholar 

  8. Ng, B., Toews, M., Durrleman, S., Shi, Y.: Shape analysis for brain structures. In: Li, S., Tavares, J.M.R.S. (eds.) Shape Analysis in Medical Image Analysis. LNCVB, vol. 14, pp. 3–49. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-03813-1_1

    Chapter  Google Scholar 

  9. Frisoni, G.B., et al.: The clinical use of structural MRI in Alzheimer disease. Nat. Rev. Neurol. 6(2), 67–77 (2010)

    Article  Google Scholar 

  10. Joshi, S., et al.: Unbiased diffeomorphic atlas construction for computational anatomy. NeuroImage 23, S151–S160 (2004)

    Article  Google Scholar 

  11. Ma, J., et al.: Bayesian template estimation in computational anatomy. NeuroImage 42(1), 252–261 (2008)

    Article  Google Scholar 

  12. Grenander, U., Miller, M.I.: Computational anatomy: an emerging discipline. Q. Appl. Math. 56(4), 617–694 (1998)

    Article  MathSciNet  Google Scholar 

  13. Sandor, S., Leahy, R.: Surface-based labeling of cortical anatomy using a deformable atlas. IEEE Trans. Med. Imaging 16(1), 41–54 (1997)

    Article  Google Scholar 

  14. Ashburner, J., Friston, K.J.: Voxel-based morphometry-the methods. Neuroimage 11(6), 805–821 (2000)

    Article  Google Scholar 

  15. Oliveira, F.P., Tavares, J.M.R.: Medical image registration: a review. Comput. Methods Biomech. Biomed. Eng. 17(2), 73–93 (2014)

    Article  Google Scholar 

  16. Ribbens, A., et al.: Unsupervised segmentation, clustering, and groupwise registration of heterogeneous populations of brain MR images. IEEE Trans. Med. Imaging 33(2), 201–224 (2013)

    Article  Google Scholar 

  17. Rueckert, D., et al.: Nonrigid registration using free-form deformations: application to breast MR images. IEEE Trans. Med. Imaging 18(8), 712–721 (1999)

    Article  Google Scholar 

  18. Bajcsy, R., Kovačič, S.: Multiresolution elastic matching. Comput. Vis. Graph. Image Process. 46(1), 1–21 (1989)

    Article  Google Scholar 

  19. Horn, B.K.P, Schunck, B.G.: Determining optical flow. In: Techniques and Applications of Image Understanding, vol. 281. International Society for Optics and Photonics (1981)

    Google Scholar 

  20. Thirion, J.-P.: Image matching as a diffusion process: an analogy with Maxwell’s demons. Med. Image Anal. 2, 243–260 (1998)

    Article  Google Scholar 

  21. Beg, M.F., et al.: Computing large deformation metric mappings via geodesic flows of diffeomorphisms. Int. J. Comput. Vis. 61(2), 139–157 (2005)

    Article  Google Scholar 

  22. Joshi, S.C., Miller, M.I.: Landmark matching via large deformation diffeomorphisms. IEEE Trans. Image Process. 9(8), 1357–1370 (2000)

    Article  MathSciNet  Google Scholar 

  23. Ashburner, J.: A fast diffeomorphic image registration algorithm. Neuroimage 38(1), 95–113 (2007)

    Article  Google Scholar 

  24. Avants, B.B., et al.: Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain. Med. Image Anal. 12(1), 26–41 (2008)

    Article  Google Scholar 

  25. Vercauteren, T., et al.: Diffeomorphic demons: efficient non-parametric image registration. NeuroImage 45(1), S61–S72 (2009)

    Article  Google Scholar 

  26. Balakrishnan, G., et al.: VoxelMorph: a learning framework for deformable medical image registration. IEEE Trans. Med. Imaging 38(8), 1788–1800 (2019)

    Article  Google Scholar 

  27. de Vos, B.D., Berendsen, F.F., Viergever, M.A., Staring, M., Išgum, I.: End-to-end unsupervised deformable image registration with a convolutional neural network. In: Cardoso, M.J., et al. (eds.) DLMIA/ML-CDS -2017. LNCS, vol. 10553, pp. 204–212. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-67558-9_24

    Chapter  Google Scholar 

  28. Sokooti, H., de Vos, B., Berendsen, F., Lelieveldt, B.P.F., Išgum, I., Staring, M.: Nonrigid image registration using multi-scale 3D convolutional neural networks. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10433, pp. 232–239. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-66182-7_27

    Chapter  Google Scholar 

  29. Dalca, A., et al.: Learning conditional deformable templates with convolutional networks. In: Advances in Neural Information Processing Systems (2019)

    Google Scholar 

  30. Sabuncu, M.R., Balci, S.K., Golland, P.: Discovering modes of an image population through mixture modeling. In: Metaxas, D., Axel, L., Fichtinger, G., Székely, G. (eds.) MICCAI 2008. LNCS, vol. 5242, pp. 381–389. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-85990-1_46

    Chapter  Google Scholar 

  31. Dalca, A.V., et al.: Unsupervised learning of probabilistic diffeomorphic registration for images and surfaces. Med. Image Anal. 57, 226–236 (2019)

    Article  Google Scholar 

  32. Milletari, F., Navab, N., Ahmadi, S.-A.: V-net: fully convolutional neural networks for volumetric medical image segmentation. In: 2016 Fourth International Conference on 3D Vision (3DV). IEEE (2016)

    Google Scholar 

  33. Marcus, D.S., et al.: Open access series of imaging studies (OASIS): cross-sectional MRI data in young, middle aged, nondemented, and demented older adults. J. Cogn. Neurosci. 19, 1498–1507 (2007)

    Article  Google Scholar 

  34. Fischl, B.: Freesurfer. Neuroimage 62(2), 774–781 (2012)

    Article  Google Scholar 

  35. Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013)

  36. Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)

  37. Gollub, R.L., et al.: The MCIC collection: a shared repository of multi-modal, multi-site brain image data from a clinical investigation of schizophrenia. Neuroinformatics 11(3), 367–388 (2013)

    Article  Google Scholar 

  38. Puonti, O., Iglesias, J.E., Van Leemput, K.: Fast and sequence-adaptivewhole-brain segmentation using parametric bayesian modeling. NeuroImage 143, 235–249 (2016)

    Article  Google Scholar 

  39. Arsigny, V., Commowick, O., Pennec, X., Ayache, N.: A log-euclidean framework for statistics on diffeomorphisms. In: Larsen, R., Nielsen, M., Sporring, J. (eds.) MICCAI 2006. LNCS, vol. 4190, pp. 924–931. Springer, Heidelberg (2006). https://doi.org/10.1007/11866565_113

    Chapter  Google Scholar 

  40. Adler, D.H., et al.: Characterizing the human hippocampus in aging and Alzheimer’s disease using a computational atlas derived from ex vivo MRI and histology. Proc. Nat. Acad. Sci. 115(16), 4252–4257 (2018)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Nancy E. and Peter C. Meinig School of Biomedical Engineering, Cornell University, Ithaca, USA

    Evan M. Yu & Mert R. Sabuncu

  2. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, USA

    Adrian V. Dalca

  3. Computer Science and Artificial Intelligence Laboratory (CSAIL), MIT, Cambridge, USA

    Adrian V. Dalca

  4. School of Electrical and Computer Engineering, Cornell University, Ithaca, USA

    Mert R. Sabuncu

Authors
  1. Evan M. Yu
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  2. Adrian V. Dalca
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  3. Mert R. Sabuncu
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Corresponding author

Correspondence to Evan M. Yu .

Editor information

Editors and Affiliations

  1. University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Mingxia Liu

  2. Rensselaer Polytechnic Institute, Troy, NY, USA

    Pingkun Yan

  3. University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Chunfeng Lian

  4. United Imaging Intelligence, Shanghai, China

    Xiaohuan Cao

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Yu, E.M., Dalca, A.V., Sabuncu, M.R. (2020). Learning Conditional Deformable Shape Templates for Brain Anatomy. In: Liu, M., Yan, P., Lian, C., Cao, X. (eds) Machine Learning in Medical Imaging. MLMI 2020. Lecture Notes in Computer Science(), vol 12436. Springer, Cham. https://doi.org/10.1007/978-3-030-59861-7_36

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  • DOI: https://doi.org/10.1007/978-3-030-59861-7_36

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-59860-0

  • Online ISBN: 978-3-030-59861-7

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