Name
Title | ||
|---|---|---|
Mapping Histological Slice Sequences to the Allen Mouse Brain Atlas Without 3D Reconstruction. |
Abstract | ||
|---|---|---|
Histological brain slices are widely used in neuroscience to study the anatomical organization of neural circuits. Systematic and accurate comparisons of anatomical data from multiple brains, especially from different studies, can benefit tremendously from registering histological slices onto a common reference atlas. Most existing methods rely on an initial reconstruction of the volume before registering it to a reference atlas. Because these slices are prone to distortions during the sectioning process and often sectioned with nonstandard angles, reconstruction is challenging and often inaccurate. Here we describe a framework that maps each slice to its corresponding plane in the Allen Mouse Brain Atlas (2015) to build a planewise mapping and then perform 2D nonrigid registration to build a pixel-wise mapping. We use the L2 norm of the histogram of oriented gradients difference of two patches as the similarity metric for both steps and a Markov random field formulation that incorporates tissue coherency to compute the nonrigid registration. To fix significantly distorted regions that are misshaped or much smaller than the control grids, we train a contextaggregation network to segment and warp them to their corresponding regions with thin plate spline. We have shown that our method generates results comparable to an expert neuroscientist and is significantly better than reconstruction-first approaches. Code and sample dataset are available at sites.google.com/view/brain-mapping. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.3389/fninf.2018.00093 | FRONTIERS IN NEUROINFORMATICS |
Keywords | Field | DocType |
nonrigid,image registration,Markov random field,histological images,2D to 3D,Allen Mouse Brain Atlas,histogram of oriented gradients | Data mining,Brain atlas,Thin plate spline,Pattern recognition,Markov random field,Computer science,Atlas (anatomy),Histogram of oriented gradients,Artificial intelligence,Norm (mathematics),Image registration,3D reconstruction | Journal |
Volume | ISSN | Citations |
12 | 1662-5196 | 0 |
PageRank | References | Authors |
0.34 | 19 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jing Xiong | 1 | 4 | 4.15 |
| Jing Ren | 2 | 0 | 0.34 |
| Liqun Luo | 3 | 0 | 0.34 |
| Mark Horowitz | 4 | 6374 | 1543.34 |