Skip to main content
SpringerLink
Log in

3D Mapping of Serial Histology Sections with Anomalies Using a Novel Robust Deformable Registration Algorithm

  • Conference paper
  • First Online:
Book cover Multimodal Brain Image Analysis and Mathematical Foundations of Computational Anatomy (MBIA 2019, MFCA 2019)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11846))

Abstract

The neuroimaging field is moving toward micron scale and molecular features in digital pathology and animal models. These require mapping to common coordinates for annotation, statistical analysis, and collaboration. An important example, the BRAIN Initiative Cell Census Network, is generating 3D brain cell atlases in mouse, and ultimately primate and human.

We aim to establish RNAseq profiles from single neurons and nuclei across the mouse brain, mapped to Allen Common Coordinate Framework (CCF). Imaging includes \(\sim \)500 tape-transfer cut 20 \(\upmu \)m thick Nissl-stained slices per brain. In key areas 100 \(\upmu \)m thick slices with 0.5–2 mm diameter circular regions punched out for snRNAseq are imaged. These contain abnormalities including contrast changes and missing tissue, two challenges not jointly addressed in diffeomorphic image registration.

Existing methods for mapping 3D images to histology require manual steps unacceptable for high throughput, or are sensitive to damaged tissue. Our approach jointly: registers 3D CCF to 2D slices, models contrast changes, estimates abnormality locations. Our registration uses 4 unknown deformations: 3D diffeomorphism, 3D affine, 2D diffeomorphism per-slice, 2D rigid per-slice. Contrast changes are modeled using unknown cubic polynomials per-slice. Abnormalities are estimated using Gaussian mixture modeling. The Expectation Maximization algorithm is used iteratively, with E step: compute posterior probabilities of abnormality, M step: registration and intensity transformation minimizing posterior-weighted sum-of-square-error.

We produce per-slice anatomical labels using Allen Institute’s ontology, and publicly distribute results online, with several typical and abnormal slices shown here. This work has further applications in digital pathology, and 3D brain mapping with stroke, multiple sclerosis, or other abnormalities.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Adler, D.H., et al.: Histology-derived volumetric annotation of the human hippocampal subfields in postmortem mri. Neuroimage 84, 505–523 (2014)

    Article  Google Scholar 

  2. Agarwal, N., Xu, X., Gopi, M.: Geometry processing of conventionally produced mouse brain slice images. J. Neurosci. Meth. 306, 45–56 (2018)

    Article  Google Scholar 

  3. Ali, S., Wörz, S., Amunts, K., Eils, R., Axer, M., Rohr, K.: Rigid and non-rigid registration of polarized light imaging data for 3D reconstruction of the temporal lobe of the human brain at micrometer resolution. Neuroimage 181, 235–251 (2018)

    Article  Google Scholar 

  4. Avants, B.B., Grossman, M., Gee, J.C.: Symmetric diffeomorphic image registration: evaluating automated labeling of elderly and neurodegenerative cortex and frontal lobe. In: Pluim, J.P.W., Likar, B., Gerritsen, F.A. (eds.) WBIR 2006. LNCS, vol. 4057, pp. 50–57. Springer, Heidelberg (2006). https://doi.org/10.1007/11784012_7

    Chapter  Google Scholar 

  5. Avants, B.B., Tustison, N.J., Song, G., Cook, P.A., Klein, A., Gee, J.C.: A reproducible evaluation of ants similarity metric performance in brain image registration. Neuroimage 54(3), 2033–2044 (2011)

    Article  Google Scholar 

  6. Avants, B.B., Tustison, N.J., Stauffer, M., Song, G., Wu, B., Gee, J.C.: The insight toolkit image registration framework. Front. Neuroinform. 8, 44 (2014)

    Article  Google Scholar 

  7. Bashiri, F., Baghaie, A., Rostami, R., Yu, Z., D’Souza, R.: Multi-modal medical image registration with full or partial data: a manifold learning approach. J. Imag. 5(1), 5 (2019)

    Article  Google Scholar 

  8. Beg, M.F., Miller, M.I., Trouvé, A., Younes, L.: Computing large deformation metric mappings via geodesic flows of diffeomorphisms. Int. J. Comput. Vis. 61(2), 139–157 (2005)

    Article  Google Scholar 

  9. Brett, M., Leff, A.P., Rorden, C., Ashburner, J.: Spatial normalization of brain images with focal lesions using cost function masking. Neuroimage 14(2), 486–500 (2001)

    Article  Google Scholar 

  10. Brodmann, K.: Vergleichende Lokalisationslehre der Grosshirnrinde in ihren Prinzipien dargestellt auf Grund des Zellenbaues. Barth (1909)

    Google Scholar 

  11. Chitphakdithai, N., Duncan, J.S.: Non-rigid registration with missing correspondences in preoperative and postresection brain images. In: Jiang, T., Navab, N., Pluim, J.P.W., Viergever, M.A. (eds.) MICCAI 2010. LNCS, vol. 6361, pp. 367–374. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-15705-9_45

    Chapter  Google Scholar 

  12. Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum likelihood from incomplete data via the em algorithm. J. Royal Stat. Soc. Ser. B (Methodological) 39(1), 1–38 (1977)

    MathSciNet  MATH  Google Scholar 

  13. Dong, H.W.: The Allen Reference Atlas: A Digital Color Brain Atlas of the C57Bl/6J Male Mouse. John Wiley & Sons Inc, Hoboken (2008)

    Google Scholar 

  14. Hagmann, P., et al.: Mapping the structural core of human cerebral cortex. PLoS Biol. 6(7), e159 (2008)

    Article  Google Scholar 

  15. Heinrich, M.P., et al.: Mind: modality independent neighbourhood descriptor for multi-modal deformable registration. Med. image Anal. 16(7), 1423–1435 (2012)

    Article  Google Scholar 

  16. Hyman, B.T., et al.: National institute on aging-alzheimer’s association guidelines for the neuropathologic assessment of alzheimer’s disease. Alzheimer’s Dement. 8(1), 1–13 (2012)

    Article  Google Scholar 

  17. Jiang, X., et al.: Histological analysis of gfp expression in murine bone. J. Histochem. Cytochem. 53(5), 593–602 (2005)

    Article  Google Scholar 

  18. Kasthuri, N., Lichtman, J.W.: The rise of the’projectome’. Nat. Meth. 4(4), 307 (2007)

    Article  Google Scholar 

  19. Lee, B.C., Tward, D.J., Mitra, P.P., Miller, M.I.: On variational solutions for whole brain serial-section histology using a Sobolev prior in the computational anatomy random orbit model. PLoS Comput. Biol. 14(12), e1006610 (2018)

    Article  Google Scholar 

  20. Lein, E.S., et al.: Genome-wide atlas of gene expression in the adult mouse brain. Nature 445(7124), 168 (2007)

    Article  Google Scholar 

  21. Macosko, E.Z., et al.: Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell 161(5), 1202–1214 (2015)

    Article  Google Scholar 

  22. Maes, F., Collignon, A., Vandermeulen, D., Marchal, G., Suetens, P.: Multimodality image registration by maximization of mutual information. IEEE Trans. Med. Imag. 16(2), 187–198 (1997)

    Article  Google Scholar 

  23. Mattes, D., Haynor, D.R., Vesselle, H., Lewellen, T.K., Eubank, W.: Pet-ct image registration in the chest using free-form deformations. IEEE Trans. Med. Imag. 22(1), 120–128 (2003)

    Article  Google Scholar 

  24. Miller, M.I., Trouvé, A., Younes, L.: On the metrics and euler-lagrange equations of computational anatomy. Annu. Rev. Biomed. Eng. 4(1), 375–405 (2002)

    Article  Google Scholar 

  25. Niethammer, M., et al.: Geometric metamorphosis. In: Fichtinger, G., Martel, A., Peters, T. (eds.) MICCAI 2011. LNCS, vol. 6892, pp. 639–646. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-23629-7_78

    Chapter  Google Scholar 

  26. Nithiananthan, S., et al.: Extra-dimensional demons: a method for incorporating missing tissue in deformable image registration. Med. Phys. 39(9), 5718–5731 (2012)

    Article  Google Scholar 

  27. Periaswamy, S., Farid, H.: Medical image registration with partial data. Med. Image Anal. 10(3), 452–464 (2006)

    Article  Google Scholar 

  28. Pichat, J., Iglesias, J.E., Yousry, T., Ourselin, S., Modat, M.: A survey of methods for 3D histology reconstruction. Med. Image Anal. 46, 73–105 (2018)

    Article  Google Scholar 

  29. Pinskiy, V., Jones, J., Tolpygo, A.S., Franciotti, N., Weber, K., Mitra, P.P.: High-throughput method of whole-brain sectioning, using the tape-transfer technique. PLoS One 10(7), e0102363 (2015)

    Article  Google Scholar 

  30. Pluim, J.P.W., Maintz, J.B.A., Viergever, M.A.: Mutual-information-based registration of medical images: a survey. IEEE Trans. Med. Imag. 22(8), 986–1004 (2003). https://doi.org/10.1109/TMI.2003.815867

    Article  Google Scholar 

  31. Rubinov, M., Sporns, O.: Complex network measures of brain connectivity: uses and interpretations. Neuroimage 52(3), 1059–1069 (2010)

    Article  Google Scholar 

  32. Salie, R., Li, H., Jiang, X., Rowe, D.W., Kalajzic, I., Susa, M.: A rapid, nonradioactive in situ hybridization technique for use on cryosectioned adult mouse bone. Calcified Tissue Int. 83(3), 212–221 (2008)

    Article  Google Scholar 

  33. Sdika, M., Pelletier, D.: Nonrigid registration of multiple sclerosis brain images using lesion inpainting for morphometry or lesion mapping. Hum. Brain Map. 30(4), 1060–1067 (2009)

    Article  Google Scholar 

  34. Staniforth, A., Côté, J.: Semi-lagrangian integration schemes for atmospheric models–a review. Mon. Weather Rev. 119(9), 2206–2223 (1991)

    Article  Google Scholar 

  35. Stefanescu, R., et al.: Non-rigid atlas to subject registration with pathologies for conformal brain radiotherapy. In: Barillot, C., Haynor, D.R., Hellier, P. (eds.) MICCAI 2004. LNCS, vol. 3216, pp. 704–711. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-30135-6_86

    Chapter  Google Scholar 

  36. Taniguchi, H., et al.: A resource of cre driver lines for genetic targeting of gabaergic neurons in cerebral cortex. Neuron 71(6), 995–1013 (2011)

    Article  Google Scholar 

  37. Towns, J., et al.: Xsede: accelerating scientific discovery. Comput. Sci. Eng. 16(5), 62–74 (2014)

    Article  Google Scholar 

  38. Tward, D., Miller, M., Trouve, A., Younes, L.: Parametric surface diffeomorphometry for low dimensional embeddings of dense segmentations and imagery. IEEE Trans. Pattern Anal. Mach. Intell. 39(6), 1195–1208 (2017)

    Article  Google Scholar 

  39. Tward, D.J., et al.: Diffeomorphic registration with intensity transformation and missing data: Application to 3D digital pathology of Alzheimer’s disease. BioRxiv, p. 494005 (2019)

    Google Scholar 

  40. Vidal, C., Hewitt, J., Davis, S., Younes, L., Jain, S., Jedynak, B.: Template registration with missing parts: application to the segmentation of m. tuberculosis infected lungs. In: 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 718–721. IEEE (2009)

    Google Scholar 

  41. Wachinger, C., Navab, N.: Entropy and laplacian images: structural representations for multi-modal registration. Med. Image Anal. 16(1), 1–17 (2012)

    Article  Google Scholar 

  42. Wu, J., Tang, X.: Fast diffeomorphic image registration via gpu-based parallel computing: an investigation of the matching cost function. In: Proceedings of SPIE Medical Imaging (SPIE-MI) (February 2018)

    Google Scholar 

  43. Xiong, J., Ren, J., Luo, L., Horowitz, M.: Mapping histological slice sequences to the allen mouse brain atlas without 3D reconstruction. Front. Neuroinform. 12, 93 (2018)

    Article  Google Scholar 

  44. Yoo, T.S., et al.: Engineering and algorithm design for an image processing api: a technical report on itk-the insight toolkit. Stud. Health Technol. Inform. 85, 586–592 (2002)

    Google Scholar 

  45. Zacharaki, E.I., Shen, D., Lee, S.K., Davatzikos, C.: Orbit: a multiresolution framework for deformable registration of brain tumor images. IEEE Trans. Med. Imag. 27(8), 1003–1017 (2008)

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by the National Institutes of Health P41EB015909, RO1NS086888, R01EB020062, R01NS102670, U19AG033655, R01MH105660, U19MH114821, U01MH114824; National Science Foundation 16-569 NeuroNex contract 1707298; Computational Anatomy Science Gateway as part of the Extreme Science and Engineering Discovery Environment [37] (NSF ACI1548562); the Kavli Neuroscience Discovery Institute supported by the Kavli Foundation, the Crick-Clay Professorship, CSHL; and the H. N. Mahabala Chair, IIT Madras.

Author information

Authors and Affiliations

  1. Johns Hopkins University, Baltimore, MD, 21218, USA

    Daniel Tward, Brian Lee & Michael Miller

  2. Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, 11724, USA

    Xu Li, Bingxing Huo & Partha Mitra

Authors
  1. Daniel Tward
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xu Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Bingxing Huo
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Brian Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Partha Mitra
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Michael Miller
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Daniel Tward .

Editor information

Editors and Affiliations

  1. The University of Texas at Arlington, Arlington, TX, USA

    Dajiang Zhu

  2. Indiana University – Purdue University Indianapolis, Indianapolis, IN, USA

    Jingwen Yan

  3. University of Pittsburgh, Pittsburgh, PA, USA

    Heng Huang

  4. University of Pennsylvania, Philadelphia, PA, USA

    Li Shen

  5. University of Southern California, Marina Del Rey, CA, USA

    Paul M. Thompson

  6. Harvard Medical School, Boston, MA, USA

    Carl-Fredrik Westin

  7. Inria Sophia-Antipolis, Sophia-Antipolis, France

    Xavier Pennec

  8. University of Utah, Salt Lake City, UT, USA

    Sarang Joshi

  9. University of Copenhagen, Copenhagen, Denmark

    Mads Nielsen

  10. University of Virginia, Charlottesville, VA, USA

    Tom Fletcher

  11. Inria, Paris, France

    Stanley Durrleman

  12. University of Copenhagen, Copenhagen, Denmark

    Stefan Sommer

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Tward, D., Li, X., Huo, B., Lee, B., Mitra, P., Miller, M. (2019). 3D Mapping of Serial Histology Sections with Anomalies Using a Novel Robust Deformable Registration Algorithm. In: Zhu, D., et al. Multimodal Brain Image Analysis and Mathematical Foundations of Computational Anatomy. MBIA MFCA 2019 2019. Lecture Notes in Computer Science(), vol 11846. Springer, Cham. https://doi.org/10.1007/978-3-030-33226-6_18

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-33226-6_18

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-33225-9

  • Online ISBN: 978-3-030-33226-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Societies and partnerships

Search

Navigation