Name
Title | ||
|---|---|---|
3d Mapping Of Serial Histology Sections With Anomalies Using A Novel Robust Deformable Registration Algorithm |
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 similar to 500 tape-transfer cut 20 mu m thick Nissl-stained slices per brain. In key areas 100 mu m thick slices with 0.5-2mm 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. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/978-3-030-33226-6_18 | MULTIMODAL BRAIN IMAGE ANALYSIS AND MATHEMATICAL FOUNDATIONS OF COMPUTATIONAL ANATOMY |
Keywords | DocType | Volume |
Neuroimaging, Image registration, Histology | Conference | 11846 |
ISSN | Citations | PageRank |
0302-9743 | 0 | 0.34 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Daniel Jacob Tward | 1 | 7 | 5.19 |
| Xu Li | 2 | 0 | 0.68 |
| Bing-Xing Huo | 3 | 0 | 0.68 |
| Brian C. Lee | 4 | 0 | 1.69 |
| Partha P Mitra | 5 | 229 | 30.31 |
| Michael I Miller | 6 | 3123 | 422.82 |