Abstract
Atlases are crucial to imaging statistics as they enable the standardization of inter-subject and inter-population analyses. While existing atlas estimation methods based on fluid/elastic/diffusion registration yield high-quality results for the human brain, these deformation models do not extend to a variety of other challenging areas of neuroscience such as the anatomy of C. elegans worms and fruit flies. To this end, this work presents a general probabilistic deep network-based framework for atlas estimation and registration which can flexibly incorporate various deformation models and levels of keypoint supervision that can be applied to a wide class of model organisms. Of particular relevance, it also develops a deformable piecewise rigid atlas model which is regularized to preserve inter-observation distances between neighbors. These modeling considerations are shown to improve atlas construction and key-point alignment across a diversity of datasets with small sample sizes including neuron positions in C. elegans hermaphrodites, fluorescence microscopy of male C. elegans, and images of fruit fly wings. Code is accessible at https://github.com/amin-nejat/Deformable-Atlas.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
Dirichlet is an appropriate choice for color distribution as its samples sum to one and prior information about expected color can be encoded in its parameters \(\boldsymbol{\alpha }\).
References
Arsigny, V., Commowick, O., Ayache, N., Pennec, X.: A fast and log-Euclidean polyaffine framework for locally linear registration. J. Math. Imaging Vis. 33(2), 222–238 (2009)
Arsigny, V., Pennec, X., Ayache, N.: Polyrigid and polyaffine transformations: a new class of diffeomorphisms for locally rigid or affine registration. In: Ellis, R.E., Peters, T.M. (eds.) MICCAI 2003. LNCS, vol. 2879, pp. 829–837. Springer, Heidelberg (2003). https://doi.org/10.1007/978-3-540-39903-2_101
Avants, B., Gee, J.C.: Geodesic estimation for large deformation anatomical shape averaging and interpolation. Neuroimage 23, S139–S150 (2004)
Avants, B.B., et al.: The optimal template effect in hippocampus studies of diseased populations. Neuroimage 49(3), 2457–2466 (2010)
Bingham, E., et al.: Pyro: Deep universal probabilistic programming. J. Mach. Learn. Res. 20, 28:1–28:6 (2019)
Bubnis, G., Ban, S., DiFranco, M.D., Kato, S.: A probabilistic atlas for cell identification (2019)
Choe, K.P., Strange, K.: Molecular and genetic characterization of osmosensing and signal transduction in the nematode Caenorhabditis elegans. FEBS J. 274(22), 5782–5789 (2007)
Commowick, O., et al.: An efficient locally affine framework for the smooth registration of anatomical structures. Med. Image Anal. 12(4), 427–441 (2008)
Dalca, A., Rakic, M., Guttag, J., Sabuncu, M.: Learning conditional deformable templates with convolutional networks. In: Advances in Neural Information Processing Systems. vol. 32 (2019)
Davis, B.C., Fletcher, P.T., Bullitt, E., Joshi, S.: Population shape regression from random design data. Int. J. Comput. Vis. 90(2), 255–266 (2010)
Dey, N., Messinger, J., Smith, R.T., Curcio, C.A., Gerig, G.: Robust non-negative tensor factorization, diffeomorphic motion correction, and functional statistics to understand fixation in fluorescence microscopy. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11764, pp. 658–666. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32239-7_73
Dey, N., Ren, M., Dalca, A.V., Gerig, G.: Generative adversarial registration for improved conditional deformable templates. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 3929–3941 (2021)
Ding, Z., Niethammer, M.: Aladdin: Joint atlas building and diffeomorphic registration learning with pairwise alignment. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 20784–20793 (2022)
Emmons, S.W., Sternberg, P.W.: Male development and mating behavior (2011)
Greitz, T., Bohm, C., Holte, S., Eriksson, L.: A computerized brain atlas: construction, anatomical content, and some applications. J. Comput. Assist. Tomogr. 15(1), 26–38 (1991)
Heckscher, E.S., et al.: Atlas-builder software and the eNeuro atlas: resources for developmental biology and neuroscience. Development 141(12), 2524–2532 (2014)
Houle, D., Govindaraju, D.R., Omholt, S.: Phenomics: the next challenge. Nat. Rev. Genet. 11(12), 855–866 (2010)
Jones, A.R., Overly, C.C., Sunkin, S.M.: The Allen brain atlas: 5 years and beyond. Nat. Rev. Neurosci. 10(11), 821–828 (2009)
Joshi, S., Davis, B., Jomier, M., Gerig, G.: Unbiased diffeomorphic atlas construction for computational anatomy. Neuroimage 23, S151–S160 (2004)
Kaiser, M., Hilgetag, C.C.: Nonoptimal component placement, but short processing paths, due to long-distance projections in neural systems. PLoS Comput. Biol. 2(7), e95 (2006)
Long, F., Peng, H., Liu, X., Kim, S.K., Myers, E.: A 3D digital atlas of C. elegans and its application to single-cell analyses. Nature Meth. 6(9), 667–672 (2009)
Roland, P., et al.: Human brain atlas: for high-resolution functional and anatomical mapping. Hum. Brain Mapp. 1, 137–184 (1994)
Scheffer, L.K., Meinertzhagen, I.A.: The fly brain atlas. Annu. Rev. Cell Dev. Biol. 35, 637–653 (2019)
Schuh, A., et al.: Unbiased construction of a temporally consistent morphological atlas of neonatal brain development. bioRxiv, p. 251512 (2018)
Skuhersky, M., Wu, T., Yemini, E., Boyden, E., Tegmark, M.: Toward a more accurate 3D atlas of c. elegans neurons. bioRxiv (2021)
Sonnenschein, A., VanderZee, D., Pitchers, W.R., Chari, S., Dworkin, I.: An image database of drosophila melanogaster wings for phenomic and biometric analysis. GigaScience 4(1), s13742-015 (2015)
Sulston, J.E., Horvitz, H.R.: Post-embryonic cell lineages of the nematode, Caenorhabditis elegans. Dev. Biol. 56(1), 110–156 (1977)
Szigeti, B., et al.: OpenWorm: an open-science approach to modeling Caenorhabditis elegans. Front. Comput. Neurosci. 8, 137 (2014)
Tekieli, T., et al.: Visualizing the organization and differentiation of the male-specific nervous system of C. elegans. Development, 148, dev199687 (2021)
Toyoshima, Y., et al.: An annotation dataset facilitates automatic annotation of whole-brain activity imaging of C. elegans. bioRxiv (2019). https://doi.org/10.1101/698241
Varol, E., et al.: Statistical atlas of C. elegans neurons. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12265, pp. 119–129. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-59722-1_12
Wustenberg, R.: Carpal bone rigid-body kinematics by log-euclidean polyrigid estimation (2022)
Yemini, E., et al.: Neuropal: a multicolor atlas for whole-brain neuronal identification in C. elegans. Cell 184(1), 272–288 (2021)
Yu, J., et al.: Versatile multiple object tracking in sparse 2D/3D videos via diffeomorphic image registration. bioRxiv (2022)
Acknowledgements
Paninski: NSF NeuroNex DBI-1707398, Gatsby Charitable Foundation, DMS 1912194, Simons Foundation Collab. on the Global Brain. Yemini: Klingenstein-Simons Fellowship in Neuroscience, Hypothesis Fund. Dey: NIH NIBIB NAC P41EB015902, NIBIB 5R01EB032708. Varol: 1K99MH128772-01A1. Venkatachalam: Burroughs Wellcome Fund and NIH R01 NS126334.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
1 Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Nejatbakhsh, A., Dey, N., Venkatachalam, V., Yemini, E., Paninski, L., Varol, E. (2023). Learning Probabilistic Piecewise Rigid Atlases of Model Organisms via Generative Deep Networks. In: Frangi, A., de Bruijne, M., Wassermann, D., Navab, N. (eds) Information Processing in Medical Imaging. IPMI 2023. Lecture Notes in Computer Science, vol 13939. Springer, Cham. https://doi.org/10.1007/978-3-031-34048-2_26
Download citation
DOI: https://doi.org/10.1007/978-3-031-34048-2_26
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-34047-5
Online ISBN: 978-3-031-34048-2
eBook Packages: Computer ScienceComputer Science (R0)