Abstract
BackgroundBrain mapping research in most neuroanatomical laboratories relies on conventional processing techniques, which often introduce histological artifacts such as tissue tears and tissue loss. New methodIn 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. ResultsOur 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 one of the application we count the number of neurons in various anatomical regions using a dataset of 51 microscopic slices from a single mouse brain. Comparison with existing method(s)To the best of our knowledge the presented work is the first that automatically registers both clean as well as highly damaged high-resolutions histological slices of mouse brain to a 3D annotated reference atlas space. ConclusionsThis work represents a significant contribution to this subfield of neuroscience as it provides tools to neuroanatomist for analyzing and processing histological data.
Highlights
Understanding the brain connectome or the wiring diagram of the brain is essential to understand how the brain circuits work [48, 29]
As one of the application we count the number of neurons in various anatomical regions using a dataset of 51 microscopic slices from a single mouse brain
In order to compute the microscopic-edge image (MEI) from microscopic slice image (MI), whose edges correspond to edges in atlas-edge image (AEI), we propose a novel dominant edge detection (DED) algorithm that is a variant of the Canny edge detector
Summary
Understanding the brain connectome or the wiring diagram of the brain is essential to understand how the brain circuits work [48, 29]. Obtaining the wiring diagram of Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. The human brain is extremely difficult as it is large and contains billions of neurons forming complex interconnecting networks. Obtaining the connectome of even a simple roundworm such as C. elegan, which consists of only 302 neurons took many years [51]. With the advances in both computing power and optical imaging techniques, it has become feasible to obtain the connectome of more complex brains. Brain mapping research in most neuroanatomical laboratories relies on conventional processing techniques, which often introduce histological artifacts such as tissue tears and tissue loss
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