Geometry Processing of
Conventionally Produced Mouse Brain Slice Images
Nitin Agarwal1
Xiangmin Xu2
Gopi Meenakshisundaram1
1Interactive Graphics & Visualization Lab
2Dept. of Anatomy and Neurobiology


Brain mapping research in most neuroanatomical laboratories relies on conventional processing techniques, which often introduce histological artifacts such as tissue tears and tissue loss. In this paper we present techniques and algorithms for automatic registration and 3D reconstruction of conventionally produced mouse brain slices in a standardized atlas space. This is achieved first by constructing a virtual 3D mouse brain model from annotated slices of Allen Reference Atlas (ARA). Virtual re-slicing of the reconstructed model generates ARA-based slice images corresponding to the microscopic images of histological brain sections. These image pairs are aligned using a geometric approach through contour images. Histological artifacts in the microscopic images are detected and removed using Constrained Delaunay Triangulation before performing global alignment. Finally, non-linear registration is performed by solving Laplace’s equation with Dirichlet boundary conditions. Our methods provide significant improvements over previously reported registration techniques for the tested slices in 3D space, especially on slices with significant histological artifacts. Further, as an application we count the number of neurons in various anatomical regions using a dataset of 51 microscopic slices from a single mouse brain. This work represents a significant contribution to this subfield of neuroscience as it provides tools to neuroanatomist for analyzing and processing histological data.


Data and Code

Mouse Brain Model Mesh Slicer Registration


N. Agarwal, X. Xu, M. Gopi
Geometry Processing of Conventionally Produced Mouse Brain Slice Images
Journal of Neuroscience Methods, 2018 (arXiv Preprint)
Supplementary Document

Related Work

N. Agarwal, X. Xu, M. Gopi Robust Registration of Mouse Brain Slice with Severe Histological Artifacts. In ICVGIP, 2016. [PDF]

N. Agarwal, X. Xu, M. Gopi Automatic Detection of Histological Artifacts in Mouse Brain Slice Images. In MICCAI, 2016. [PDF]


We thank memebers of the Interactive Graphics and Visualization Lab (iGravi) in particular Shuang Zhao and Jia Chen for helpful discussions. We also thank members of the Neuroanatomy Lab especially Xiaoxiao Lin and Yanjun Sun for sample preparation, imaging and collection of histological slice data. The Allen Reference Atlas images of the mouse brain were provided by Hong-Wei Dong. This project was supported in part by NIH grants (R01MH105427 and R01NS078434).

Project template was borrowed from Richard Zhang