[Paper Review] Mesoscale microscopy for micromammals: image analysis tools for understanding the rodent brain

Adam L. Tyson, Troy W. Margrie|arXiv (Cornell University)|Feb 23, 2021

Cell Image Analysis Techniques88 references5 citations

TL;DR

This paper presents a comprehensive review of open-source image analysis tools for mesoscale whole-brain microscopy in micromammals, focusing on atlas registration, segmentation, visualization, and analysis of neural structures. It proposes a collaborative framework to unify existing tools into integrated pipelines, addressing the growing need for scalable, accessible computational solutions in rodent neuroscience.

ABSTRACT

Over the last ten years, developments in whole-brain microscopy now allow for high-resolution imaging of intact brains of small rodents such as mice. These complex images contain a wealth of information, but many neuroscience laboratories do not have all of the computational knowledge and tools needed to process these data. We review recent open source tools for registration of images to atlases, and the segmentation, visualisation and analysis of brain regions and labelled structures such as neurons. Since the field lacks fully integrated analysis pipelines for all types of whole-brain microscopy analysis, we propose a pathway for tool developers to work together to meet this challenge.

Motivation & Objective

• Address the growing gap in computational expertise and tools required to process high-resolution whole-brain microscopy data from small rodents.
• Review and consolidate existing open-source software for image registration to brain atlases, segmentation, and visualization of neural structures.
• Identify the lack of fully integrated analysis pipelines as a key bottleneck in mesoscale neuroscience.
• Propose a collaborative pathway for tool developers to standardize and interoperate tools, enabling end-to-end analysis workflows.
• Support neuroscience labs without advanced computational training to effectively analyze complex mesoscale brain images.

Proposed method

• Systematic review of recent open-source tools for whole-brain microscopy data processing in micromammals.
• Focus on tools for image registration to standardized brain atlases, such as Allen Brain Atlas or Waxholm space.
• Evaluation of segmentation techniques for identifying brain regions and labeled neurons in 3D microscopy data.
• Assessment of visualization and analysis tools for morphometric and connectivity analysis of labeled structures.
• Identification of interoperability challenges and workflow gaps across existing tools.
• Proposal of a modular, collaborative development pathway to unify tools into cohesive, extensible analysis pipelines.

Experimental results

Research questions

• RQ1 What open-source tools are currently available for processing high-resolution whole-brain microscopy data in rodents?
• RQ2 How do existing tools perform in terms of registration accuracy, segmentation fidelity, and visualization capability?
• RQ3 What are the key bottlenecks in creating end-to-end analysis pipelines for mesoscale brain imaging?
• RQ4 How can tool developers collaborate to improve interoperability and streamline data processing workflows?
• RQ5 What infrastructure and standards are needed to support scalable, accessible analysis for neuroscience labs without computational expertise?

Key findings

• A wide array of open-source tools now exists for atlas registration, segmentation, and visualization of mesoscale brain images in rodents.
• Despite the availability of individual tools, no fully integrated pipeline currently exists for end-to-end analysis of whole-brain microscopy data.
• Many neuroscience labs lack the computational expertise needed to effectively use these tools, creating a barrier to data utilization.
• The field would benefit significantly from a coordinated effort to standardize interfaces and data formats across tools.
• Collaborative development of modular, interoperable tools can accelerate discovery and democratize access to mesoscale brain imaging analysis.
• The proposed pathway emphasizes community-driven development and shared infrastructure to unify existing tools into scalable analysis pipelines.