Evidence map›Paper›PMID 29689283›Full record

ArticleJournal of neuroscience methods2018

Geometry processing of conventionally produced mouse brain slice images.

Nitin Agarwal, Xiangmin Xu, M Gopi

Open access · greenAbstract read
In one paragraph

Article in Journal of neuroscience methods, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
–field-weighted citation impact, top 99% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

11 citing papers in PubMed, 0 citations in OpenAlex.

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  5. Molecular signatures of cortical expansion in the human fetal brain.bioRxiv : the preprint server for biology · 2024
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors at 1 institution in 1 country.

Nitin AgarwalDepartment of Computer Science, University of California, Irvine, CA 92697-3435, United States. Electronic address: agarwal@ics.uci.edu.
Xiangmin XuDepartment of Anatomy and Neurobiology, School of Medicine, University of California, Irvine, CA 92697-1275, United States; Department of Biomedical Engineering, University of California, Irvine, CA 92697-2715, United States; Department of Electrical Engineering and Computer Science, University of California, Irvine, CA 92697-2625, United States.
M GopiDepartment of Computer Science, University of California, Irvine, CA 92697-3435, United States.
University of California, Irvine · US

Funding

Inhibitory neuron circuit organization and function in prefrontal cortex.R01MH105427 · NIMH · UNIVERSITY OF CALIFORNIA-IRVINE · PI GOLSHANI, PEYMAN, XU, XIANGMIN · 2015 to 2019
$2.3M
Hippocampal inhibitory neuronal circuit organizationR01NS078434 · NINDS · UNIVERSITY OF CALIFORNIA-IRVINE · PI XU, XIANGMIN · 2012 to 2016
$1.6M
NIMH NIH HHS R01 MH105427NINDS NIH HHS R01 NS078434
6 · The paper itself

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.

Indexed as

AlgorithmsAnimalsArtifactsBrainConnectomeElectronic Data ProcessingImaging, Three-DimensionalInformation Storage and RetrievalMice3D visualizationHistological artifactsImage processingImage registrationMouse brain

Identifiers

PMID29689283
PMCPMC6086593
OpenAlexW2779049050

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.