The BrainGlobe initiative has three main goals: providing specific tools for analysis and visualisation, cultivating core tools to facilitate development of interoperable tools in Python, and fostering a community of neuroscientists and developers to share knowledge, build software, and engage with the scientific and open source community. The development of BrainGlobe builds upon and relies heavily on established packages from the broader scientific Python ecosystem ensuring compatibility and interoperability with other tools. Our initiative addresses the crucial need for interoperability in neuroscience research by offering a comprehensive suite of tools accessible to users across different platforms. With a focus on ease of installation and usability, our goal is to empower researchers to analyse neuroanatomical data efficiently and introduce them to the broader scientific python ecosystem.

In this talk I plan to discuss the benefits of working in a common coordinate space for image analysis and visualisation. I will begin by introducing the concept of a BrainGlobe atlas and the associated brainglobe-atlasapi package. This standard access point has enabled the emergence of an ecosystem of BrainGlobe tools, developed both internally by the core BrainGlobe team, and externally by outside contributors. Abstracting the concept of an atlas and standardising access allows the downstream tools to be species agnostic, widening the potential user pool. I will then describe brainreg and brainglobe-registration, the tools we use to register data into a BrainGlobe atlas. I will provide two examples of how our tools utilise BrainGlobe atlases to provide valuable context, based on the annotations of the atlas. The first example will be brainmapper, a pipeline that utilises brainreg and cellfinder to detect cells in large 3D volumes and output counts per anatomical region. The second example involves brainglobe-segmentation which can be used to segment objects, and transform segmentations into sample or atlas space. Lastly, I will demonstrate visualising registered multi-modal data in 3D using brainrender.

At present, the BrainGlobe team maintains 17 packages which have 100+ code contributors. The BrainGlobe Atlas API serves as a standardised framework for working with anatomical reference atlases, facilitating comparison across samples [1]. Using brainreg, 3D whole-brain imaging data can be registered to any BrainGlobe atlas [2]. Cellfinder automates cell detection in large 3D images in a computationally efficient manner [3], while common neuroanatomical segmentation issues are tackled with brainglobe-segmentation [2]. Brainrender uses vedo to enable visualisation of 3D neuroanatomical data from both public sources and user-generated data [4, 5].

Our team is continually working on addressing the needs of the community. Currently, efforts are underway to broaden the types of microscopy data registrable into the BrainGlobe ecosystem with brainglobe-registration, using elastix to register 2D slices, 3D sub-volumes, and whole brain data. Additionally, we are developing brainglobe-stitch, a package for fusing large tiled 3D imaging datasets (300+ GB). This package will be available as a napari plugin to allow efficient previewing of the fused dataset. Lastly, we are porting the functionality of brainrender to a napari plugin, brainrender-napari, to provide a cohesive analysis and visualisation environment for all BrainGlobe tools.

Concurrently, we are streamlining and enhancing the developer experience. This involves consolidating related repositories within the BrainGlobe codebase and extracting duplicated code to brainglobe-utils, a shared library. We are also intensifying efforts to improve docstring coverage and providing introductory guides for new developers, along with a development roadmap outlining planned future work.

Citations:
[1] F. Claudi, L. Petrucco, A. Tyson, T. Branco, T. Margrie, and R. Portugues, ‘BrainGlobe Atlas API: a common interface for neuroanatomical atlases’, J. Open Source Softw., vol. 5, no. 54, p. 2668, Oct. 2020, doi: 10.21105/joss.02668.
[2] A. L. Tyson et al., ‘Accurate determination of marker location within whole-brain microscopy images’, Sci. Rep., vol. 12, no. 1, p. 867, Dec. 2022, doi: 10.1038/s41598-021-04676-9.
[3] A. L. Tyson et al., ‘A deep learning algorithm for 3D cell detection in whole mouse brain image datasets’, PLOS Comput. Biol., vol. 17, no. 5, p. e1009074, May 2021, doi: 10.1371/journal.pcbi.1009074.
[4] F. Claudi, A. L. Tyson, L. Petrucco, T. W. Margrie, R. Portugues, and T. Branco, ‘Visualizing anatomically registered data with brainrender’, eLife, vol. 10, p. e65751, Mar. 2021, doi: 10.7554/eLife.65751.
[5] M. Musy et al., "vedo, a python module for scientific analysis and visualization of 3D objects and point clouds", Zenodo, 2021, doi: 10.5281/zenodo.7019968.