Localization and Registration of 2D Histological Mouse Brain Images in 3D Atlas Space.
2D in 3D localization
Deep learning
Image registration
Mouse Brain Mapping
Journal
Neuroinformatics
ISSN: 1559-0089
Titre abrégé: Neuroinformatics
Pays: United States
ID NLM: 101142069
Informations de publication
Date de publication:
07 2023
07 2023
Historique:
accepted:
12
05
2023
medline:
8
8
2023
pubmed:
26
6
2023
entrez:
25
6
2023
Statut:
ppublish
Résumé
To accurately explore the anatomical organization of neural circuits in the brain, it is crucial to map the experimental brain data onto a standardized system of coordinates. Studying 2D histological mouse brain slices remains the standard procedure in many laboratories. Mapping these 2D brain slices is challenging; due to deformations, artifacts, and tilted angles introduced during the standard preparation and slicing process. In addition, analysis of experimental mouse brain slices can be highly dependent on the level of expertise of the human operator. Here we propose a computational tool for Accurate Mouse Brain Image Analysis (AMBIA), to map 2D mouse brain slices on the 3D brain model with minimal human intervention. AMBIA has a modular design that comprises a localization module and a registration module. The localization module is a deep learning-based pipeline that localizes a single 2D slice in the 3D Allen Brain Atlas and generates a corresponding atlas plane. The registration module is built upon the Ardent python package that performs deformable 2D registration between the brain slice to its corresponding atlas. By comparing AMBIA's performance in localization and registration to human ratings, we demonstrate that it performs at a human expert level. AMBIA provides an intuitive and highly efficient way for accurate registration of experimental 2D mouse brain images to 3D digital mouse brain atlas. Our tool provides a graphical user interface and it is designed to be used by researchers with minimal programming knowledge.
Identifiants
pubmed: 37357231
doi: 10.1007/s12021-023-09632-8
pii: 10.1007/s12021-023-09632-8
pmc: PMC10406728
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
615-630Subventions
Organisme : Austrian Science Fund FWF
ID : F44-17-B23
Pays : Austria
Informations de copyright
© 2023. The Author(s).
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