Evidence map›Paper›PMID 36304320›Full record

ArticleFrontiers in bioinformatics2022

An End-To-End Pipeline for Fully Automatic Morphological Quantification of Mouse Brain Structures From MRI Imagery.

Shahinur Alam, Tae-Yeon Eom, Jeffrey Steinberg, David Ackerman, J Eric Schmitt, Walter J Akers, Stanislav S Zakharenko, Khaled Khairy

Open access · goldAbstract read
In one paragraph

Article in Frontiers in bioinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
1.1field-weighted citation impact, top 17% 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

3 citing papers in PubMed, 4 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
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

8 authors at 3 institutions in 1 country.

Shahinur AlamCenter for Bioimage Informatics, St. Jude Children's Research Hospital, Memphis, TN, United States.
Tae-Yeon EomDepartment of Developmental Neurobiology, St. Jude Children's Research Hospital, Memphis, TN, United States.
Jeffrey SteinbergCenter for in Vivo Imaging and Therapeutics, St. Jude Children's Research Hospital, Memphis, TN, United States.
David AckermanScientific Computing, Janelia Research Campus, Ashburn, VA, United States.
J Eric SchmittBrain Behavior Laboratory, Departments of Psychiatry and Radiology, University of Pennsylvania, Philadelphia, PA, United States.
Walter J AkersCenter for in Vivo Imaging and Therapeutics, St. Jude Children's Research Hospital, Memphis, TN, United States.
Stanislav S ZakharenkoDepartment of Developmental Neurobiology, St. Jude Children's Research Hospital, Memphis, TN, United States.
Khaled KhairyCenter for Bioimage Informatics, St. Jude Children's Research Hospital, Memphis, TN, United States.
St. Jude Children's Research Hospital · USJanelia Research Campus · USUniversity of Pennsylvania · US

Funding

Synaptic mechanisms of auditory memoryR01DC012833 · NIDCD · ST. JUDE CHILDREN'S RESEARCH HOSPITAL · PI ZAKHARENKO, STANISLAV S · 2013 to 2022
$4.2M
Towards understanding cellular mechanisms of positive symptoms of schizophreniaR01MH097742 · NIMH · ST. JUDE CHILDREN'S RESEARCH HOSPITAL · PI ZAKHARENKO, STANISLAV S · 2013 to 2022
$4.1M
Trans-synaptic interactions during synaptic plasticity in the hippocampusR01MH079079 · NIMH · ST. JUDE CHILDREN'S RESEARCH HOSPITAL · PI ZAKHARENKO, STANISLAV S · 2008 to 2012
$1.9M
Towards understanding cellular mechanisms of positive symptoms of schizophreniaR56MH097742 · NIMH · ST. JUDE CHILDREN'S RESEARCH HOSPITAL · PI ZAKHARENKO, STANISLAV S · 2018 to 2018
$494k
NIDCD NIH HHS R01 DC012833NIMH NIH HHS R01 MH079079NIMH NIH HHS R01 MH097742NIMH NIH HHS R56 MH097742
6 · The paper itself

Abstract

Segmentation of mouse brain magnetic resonance images (MRI) based on anatomical and/or functional features is an important step towards morphogenetic brain structure characterization of murine models in neurobiological studies. State-of-the-art image segmentation methods register image volumes to standard presegmented templates or well-characterized highly detailed image atlases. Performance of these methods depends critically on the quality of skull-stripping, which is the digital removal of tissue signal exterior to the brain. This is, however, tedious to do manually and challenging to automate. Registration-based segmentation, in addition, performs poorly on small structures, low resolution images, weak signals, or faint boundaries, intrinsic to

Indexed as

brain atlasdata augmentationdeep learningimage registrationimage segmentationmouse brainMRI

Identifiers

PMID36304320
PMCPMC9580949
OpenAlexW4293079418

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.