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

  • Daniel TwardEmail author
  • Xu Li
  • Bingxing Huo
  • Brian Lee
  • Partha Mitra
  • Michael Miller
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, 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.

Keywords

Neuroimaging Image registration Histology 

Notes

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.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Johns Hopkins UniversityBaltimoreUSA
  2. 2.Cold Spring Harbor LaboratoryCold Spring HarborUSA

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