4,490 research outputs found
Geometry Processing of Conventionally Produced Mouse Brain Slice Images
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.Comment: 14 pages, 11 figure
OReX: Object Reconstruction from Planner Cross-sections Using Neural Fields
Reconstructing 3D shapes from planar cross-sections is a challenge inspired
by downstream applications like medical imaging and geographic informatics. The
input is an in/out indicator function fully defined on a sparse collection of
planes in space, and the output is an interpolation of the indicator function
to the entire volume. Previous works addressing this sparse and ill-posed
problem either produce low quality results, or rely on additional priors such
as target topology, appearance information, or input normal directions. In this
paper, we present OReX, a method for 3D shape reconstruction from slices alone,
featuring a Neural Field as the interpolation prior. A simple neural network is
trained on the input planes to receive a 3D coordinate and return an
inside/outside estimate for the query point. This prior is powerful in inducing
smoothness and self-similarities. The main challenge for this approach is
high-frequency details, as the neural prior is overly smoothing. To alleviate
this, we offer an iterative estimation architecture and a hierarchical input
sampling scheme that encourage coarse-to-fine training, allowing focusing on
high frequencies at later stages. In addition, we identify and analyze a common
ripple-like effect stemming from the mesh extraction step. We mitigate it by
regularizing the spatial gradients of the indicator function around input
in/out boundaries, cutting the problem at the root.
Through extensive qualitative and quantitative experimentation, we
demonstrate our method is robust, accurate, and scales well with the size of
the input. We report state-of-the-art results compared to previous approaches
and recent potential solutions, and demonstrate the benefit of our individual
contributions through analysis and ablation studies
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