CloudReg: automatic terabyte-scale cross-modal brain volume registration

Abstract

Imaging methods such as magnetic resonance imaging (MRI), micro-computed tomography (microCT) and light-sheet microscopy (LSM) of cleared tissue samples can generate intact anatomic and molecular whole-brain data. However, each modality produces unique artifacts based on the physical principles of the technique, including intensity inhomogeneity due to magnetic field bias in MRI or microscope optics in LSM and beam hardening in microCT 1, 2, 3 . These artifacts and the size of the datasets generated pose a substantial challenge in data handling, cross-modal image registration, and analysis. Visualization and anatomically relevant analysis of high-resolution, multi-field-of-view (mFOV) datasets require preprocessing to remove artifacts, stitching into a complete volume, and registration to a reference atlas 3,4 .Each step presents specific challenges. First, stitching acquired fields of view (FOVs) into a complete volume is computation and time intensive. Second,

Non-invasive in situ visualization of the murine cranial vasculature

“…We used the Neurosimplicity Imaging Suite (Neurosimplicity, LLC NJ, USA), which leverages and combines automatic deformable registration such as CloudReg, an open source tool ( Chandrashekhar et al., 2021 ), with automatic segmentation/feature extraction, and visualization, such as some available open source visualization tools ( Vogelstein et al., 2018 ), to register the iteratively processed and imaged Micro-CT samples to each other and to an atlas such as the ARA CCFv3, extract features of interest, and visualize the resulting registered two-dimensional (2D) raw data and three-dimensional (3D) data. Image processing steps including 1) denoising with a Gaussian kernel, 2) binary thresholding, 3) morphological operations were applied to the raw 2D data to generate the segmentations.…”

BrainLine: An Open Pipeline for Connectivity Analysis of Heterogeneous Whole-Brain Fluorescence Volumes

“…To address these challenges, we present BrainLine, an open-source, fully-integrated pipeline that performs registration, axon segmentation, soma detection, visualization, and analysis on whole-brain fluorescence volumes (Figure 1a). BrainLine combines state-of-the-art, already available open-source tools such as CloudReg [3] and ilastik [2] with brainlit, our Python package developed here. The BrainLine pipeline uses generalizable machine learning training schemes that adapt to out-of-distribution samples and facilitates cloud-based collaboration across institutions.…”

“…BrainLine allows for efficient processing of heterogeneous whole brain fluorescence volumes. a BrainLine combines CloudReg [3], ilastik [2] and our package, brainlit, to produce results in both quantitative ( a.i ) and visual ( a.ii-a.iii ) formats. b Example images with fluorescently labeled axon projections and arrows pointing to regions with (green) and without (red) labeled axons.…”

“…To share and interact with data across multiple institutions, BrainLine uses Amazon S3 to store data in precomputed format, so it can be viewed using Neuroglancer [1]. Specifically, we use CloudReg [3] for file conversion of the stitched image, and for image registration to the Allen atlas [10].…”

Fluorescent transgenic mouse models for whole-brain imaging in health and disease

“…Simultaneous with the development of imaging technologies, the need for image processing tool sets for terabyte-sized datasets has emerged. The development of a common atlas of the mouse brain (Common Coordinate Framework v3; Wang et al, 2020), combined with image stitching (Bria and Iannello, 2012;Wang et al, 2020), transformation to multi-resolution image formats (Bria et al, 2016), and spatial registration (Tward et al, 2020;Chandrashekhar et al, 2021;Jin et al, 2022;Qu et al, 2022), allows the quantification of cell densities (Renier et al, 2016), axonal projections (Ye et al, 2016), vasculature (Kirst et al, 2020), and the reconstruction of full single neurons (Winnubst et al, 2019;Peng et al, 2021;Gao et al, 2022).…”