Localization and Registration of 2D Histological Mouse Brain Images in 3D Atlas Space.

IF 2.7 4区 医学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Neuroinformatics Pub Date : 2023-07-01 Epub Date: 2023-06-26 DOI:10.1007/s12021-023-09632-8
Maryam Sadeghi, Arnau Ramos-Prats, Pedro Neto, Federico Castaldi, Devin Crowley, Pawel Matulewicz, Enrica Paradiso, Wolfgang Freysinger, Francesco Ferraguti, Georg Goebel
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Abstract

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.

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二维组织学小鼠大脑图像在三维图集空间中的定位和配准。
要准确探索大脑神经回路的解剖组织,必须将大脑实验数据映射到标准化的坐标系统上。研究二维组织学小鼠脑切片仍然是许多实验室的标准程序。由于在标准制备和切片过程中会产生变形、伪影和倾斜角度,因此绘制这些二维脑切片具有挑战性。此外,对实验小鼠脑切片的分析还高度依赖于操作人员的专业知识水平。在这里,我们提出了一种用于精确小鼠脑图像分析(AMBIA)的计算工具,只需极少的人工干预就能将二维小鼠脑切片映射到三维脑模型上。AMBIA 采用模块化设计,包括定位模块和配准模块。定位模块是一个基于深度学习的管道,可定位三维艾伦脑图谱中的单个二维切片,并生成相应的图谱平面。配准模块建立在 Ardent python 软件包的基础上,可在大脑切片与其对应的地图集之间执行可变形的二维配准。通过将 AMBIA 的定位和配准性能与人类评分进行比较,我们证明它的性能达到了人类专家的水平。AMBIA 提供了一种直观、高效的方法,可将实验性二维小鼠脑图像精确配准到三维数字小鼠脑图谱。我们的工具提供了图形用户界面,研究人员只需具备最低限度的编程知识即可使用。
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来源期刊
Neuroinformatics
Neuroinformatics 医学-计算机:跨学科应用
CiteScore
6.00
自引率
6.70%
发文量
54
审稿时长
3 months
期刊介绍: Neuroinformatics publishes original articles and reviews with an emphasis on data structure and software tools related to analysis, modeling, integration, and sharing in all areas of neuroscience research. The editors particularly invite contributions on: (1) Theory and methodology, including discussions on ontologies, modeling approaches, database design, and meta-analyses; (2) Descriptions of developed databases and software tools, and of the methods for their distribution; (3) Relevant experimental results, such as reports accompanie by the release of massive data sets; (4) Computational simulations of models integrating and organizing complex data; and (5) Neuroengineering approaches, including hardware, robotics, and information theory studies.
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