Robust registration of Mouse brain slices with severe histological artifacts

ABSTRACT

Brain mapping research is facilitated by first aligning digital images of mouse brain slices to standardized atlas framework such as the Allen Reference Atlas (ARA). However, conventional processing of these brain slices introduces many histological artifacts such as tears and missing regions in the tissue, which make the automatic alignment process extremely challenging. We present an end-to-end fully automatic registration pipeline for alignment of digital images of mouse brain slices that may have histological artifacts, to a standardized atlas space. We use a geometric approach where we first align the bounding box of convex hulls of brain slice contours and atlas template contours, which are extracted using a variant of Canny edge detector. We then detect the artifacts using Constrained Delaunay Triangulation (CDT) and remove them from the contours before performing global alignment of points using iterative closest point (ICP). This is followed by a final non-linear registration by solving the Laplace's equation with Dirichlet boundary conditions. We tested our algorithm on 200 mouse brain slice images including slices acquired from conventional processing techniques having major histological artifacts, and from serial two-photon tomography (STPT) with no major artifacts. We show significant improvement over other registration techniques, both qualitatively and quantitatively, in all slices especially on slices with significant histological artifacts.

References

1. W. S. I. Ali et al. Registering coronal histological 2-d sections of a rat brain with coronal sections of a 3-d brain atlas using geometric curve invariants and b-spline representation. Medical Imaging, IEEE Transactions on, 17(6):957--966, 1998.
2. N. Amenta, M. Bern, and D. Eppstein. The crust and the β-skeleton: Combinatorial curve reconstruction. Graphical models and image processing, 60(2):125--135, 1998.
3. L. Bertrand and J. Nissanov. The neuroterrain 3d mouse brain atlas. Frontiers in neuroinformatics, 2:3, 2008.
4. L. P. Chew. Constrained delaunay triangulations. Algorithmica, 4(1--4):97--108, 1989.
5. A. C. Crecelius, D. S. Cornett, R. M. Caprioli, et al. Three-dimensional visualization of protein expression in mouse brain structures using imaging mass spectrometry. J. Am. Soc. Mass Spectrom., 16(7):1093--1099, 2005.
6. J. Dauguet, T. Delzescaux, F. Condé, et al. Three-dimensional reconstruction of stained histological slices and 3d non-linear registration with in-vivo mri for whole baboon brain. J. Neurosci. Meth., 164(1):191--204, 2007.
7. N. Gelfand et al. Robust global registration. In SGP, volume 2, pages 197--206, 2005.
8. S. Gottschalk, M. C. Lin, and D. Manocha. Obbtree: A hierarchical structure for rapid interference detection. In Proceedings of Annual conference on Computer graphics and interactive techniques, pages 171--180. ACM, 1996.
9. A. Hess et al. A new method for reliable and efficient reconstruction of 3d images from autoradiographs of brain sections. J. Neurosci. Meth., 84(1):77--86, 1998.
10. L. S. Hibbard and R. A. Hawkins. Objective image alignment for three-dimensional reconstruction of digital autoradiograms. J. Neurosci. Meth., 26(1):55--74, 1988.
11. I. Jolliffe. Principal component analysis. Wiley Online Library, 2002.
12. T. Ju, J. Warren, G. Eichele, C. Thaller, et al. A geometric database for gene expression data. In Symp. on Geometry Processing (SGP), pages 166--176, 2003.
13. L. M. Kindle, I. A. Kakadiaris, T. Ju, and J. P. Carson. A semiautomated approach for artefact removal in serial tissue cryosections. J. Microsc., 241(2):200--206, 2011.
14. S. Klein, M. Staring, et al. Elastix: a toolbox for intensity-based medical image registration. Medical Imaging, IEEE Transactions on, 29(1):196--205, 2010.
15. L. Kuan, Y. Li, C. Lau, D. Feng, A. Bernard, et al. Neuroinformatics of the allen mouse brain connectivity atlas. Methods, 73:4--17, 2015.
16. E. S. Lein, M. J. Hawrylycz, N. Ao, et al. Genome-wide atlas of gene expression in the adult mouse brain. Nature, 445(7124):168--176, 2007.
17. D. Levin. The approximation power of moving least-squares. Math. of Computation, 67(224):1517--1531, 1998.
18. Y. Ma et al. Three-dimensional digital atlas database of adult c57bl/6j mouse brain by magnetic resonance microscopy. Neuroscience, 135(4):1203--1215, 2005.
19. J. B. Maintz et al. A survey of medical image registration. Med Image Anal, 2(1):1--36, Mar 1998.
20. K. Mosaliganti, T. Pan, R. Sharp, et al. Registration and 3d visualization of large microscopy images. In SPIE Medical imaging, pages 61442V--61442V. International Society for Optics and Photonics, 2006.
21. L. Ng et al. Automated high-throughput registration for localizing 3d mouse brain gene expression using itk. In Insight J. MICCAI Workshop, 2005.
22. C. Nikou et al. Robust statistics-based global energy function for alignment of serially acquired autoradiographic sections. J. Neurosci. Meth., 124(1):93--102, 2003.
23. S. W. Oh, J. A. Harris, L. Ng, B. Winslow, N. Cain, et al. A mesoscale connectome of the mouse brain. Nature, 508(7495):207--214, 2014.
24. N. Otsu. A threshold selection method from gray-level histograms. Automatica, 11(285--296):23--27, 1975.
25. P. Pérez, M. Gangnet, and A. Blake. Poisson image editing. In ACM Transactions on Graphics (TOG), volume 22, pages 313--318. ACM, 2003.
26. J. P. Pluim, J. A. Maintz, et al. Mutual-information-based registration of medical images: a survey. Medical Imaging, IEEE Transactions on, 22(8):986--1004, 2003.
27. X. Qiu, T. Pridmore, and A. Pitiot. Correcting distorted histology slices for 3d reconstruction. Med Image Underst Anal, 2009.
28. T. Ragan, L. R. Kadiri, et al. Serial two-photon tomography for automated ex vivo mouse brain imaging. Nature methods, 9(3):255--258, 2012.
29. A. Rangarajan, H. Chui, et al. A robust point matching algorithm for autoradiograph alignment. Medical Image Analysis, 1(4):379--398, 1997.
30. S. J. Sawiak, G. B. Williams, N. I. Wood, et al. Spmmouse: A new toolbox for spm in the animal brain. In ISMRM 17th Scientific Meeting & Exhibition, April, pages 18--24, 2009.
31. D. W. Scott. On optimal and data-based histograms. Biometrika, 66(3):605--610, 1979.
32. Y. Sun et al. Cell-type-specific circuit connectivity of hippocampal ca1 revealed through cre-dependent rabies tracing. Cell reports, 7(1):269--280, 2014.
33. D. A. Vousden, J. Epp, H. Okuno, et al. Whole-brain mapping of behaviourally induced neural activation in mice. Brain Structure and Function, pages 1--15, 2014.
34. P. A. Yushkevich, B. B. Avants, L. Ng, et al. 3d mouse brain reconstruction from histology using a coarse-to-fine approach. In Biomedical Image Registration, pages 230--237. Springer, 2006.
35. W. Zhao, T. Y. Young, and M. D. Ginsberg. Registration and three-dimensional reconstruction of autoradiographic images by the disparity analysis method. Medical Imaging, IEEE Transactions on, 12(4):782--791, 1993.

Index Terms

• Computing methodologies

  • Artificial intelligence

    • Computer vision

      • Computer vision problems

        • Matching

  • Computer graphics

    • Image manipulation

      • Image processing

• Theory of computation

  • Randomness, geometry and discrete structures

    • Computational geometry