Dataset Information

Fast, 3D Isotropic Imaging of Whole Mouse Brain Using Multiangle-Resolved Subvoxel SPIM.

ABSTRACT: The recent integration of light-sheet microscopy and tissue-clearing has facilitated an important alternative to conventional histological imaging approaches. However, the in toto cellular mapping of neural circuits throughout an intact mouse brain remains highly challenging, requiring complicated mechanical stitching, and suffering from anisotropic resolution insufficient for high-quality reconstruction in 3D. Here, the use of a multiangle-resolved subvoxel selective plane illumination microscope (Mars-SPIM) is proposed to achieve high-throughput imaging of whole mouse brain at isotropic cellular resolution. This light-sheet imaging technique can computationally improve the spatial resolution over six times under a large field of view, eliminating the use of slow tile stitching. Furthermore, it can recover complete structural information of the sample from images subject to thick-tissue scattering/attenuation. With Mars-SPIM, a digital atlas of a cleared whole mouse brain (?7 mm × 9.5 mm × 5 mm) can readily be obtained with an isotropic resolution of ?2 µm (1 µm voxel) and a short acquisition time of 30 min. It provides an efficient way to implement system-level cellular analysis, such as the mapping of different neuron populations and tracing of long-distance neural projections over the entire brain. Mars-SPIM is thus well suited for high-throughput cell-profiling phenotyping of brain and other mammalian organs.

SUBMITTER: Nie J

PROVIDER: S-EPMC7001627 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature

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Fast, 3D Isotropic Imaging of Whole Mouse Brain Using Multiangle-Resolved Subvoxel SPIM.

Nie Jun J Liu Sa S Yu Tingting T Li Yusha Y Ping Junyu J Wan Peng P Zhao Fang F Huang Yujie Y Mei Wei W Zeng Shaoqun S Zhu Dan D Fei Peng P

Advanced science (Weinheim, Baden-Wurttemberg, Germany) 20191203 3

The recent integration of light-sheet microscopy and tissue-clearing has facilitated an important alternative to conventional histological imaging approaches. However, the in toto cellular mapping of neural circuits throughout an intact mouse brain remains highly challenging, requiring complicated mechanical stitching, and suffering from anisotropic resolution insufficient for high-quality reconstruction in 3D. Here, the use of a multiangle-resolved subvoxel selective plane illumination microsco ...[more]

PMID: 32042557

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Project description:Brain extraction is a critical pre-processing step in brain magnetic resonance imaging (MRI) analytical pipelines. In rodents, this is often achieved by manually editing brain masks slice-by-slice, a time-consuming task where workloads increase with higher spatial resolution datasets. We recently demonstrated successful automatic brain extraction via a deep-learning-based framework, U-Net, using 2D convolutions. However, such an approach cannot make use of the rich 3D spatial-context information from volumetric MRI data. In this study, we advanced our previously proposed U-Net architecture by replacing all 2D operations with their 3D counterparts and created a 3D U-Net framework. We trained and validated our model using a recently released CAMRI rat brain database acquired at isotropic spatial resolution, including T2-weighted turbo-spin-echo structural MRI and T2*-weighted echo-planar-imaging functional MRI. The performance of our 3D U-Net model was compared with existing rodent brain extraction tools, including Rapid Automatic Tissue Segmentation, Pulse-Coupled Neural Network, SHape descriptor selected External Regions after Morphologically filtering, and our previously proposed 2D U-Net model. 3D U-Net demonstrated superior performance in Dice, Jaccard, center-of-mass distance, Hausdorff distance, and sensitivity. Additionally, we demonstrated the reliability of 3D U-Net under various noise levels, evaluated the optimal training sample sizes, and disseminated all source codes publicly, with a hope that this approach will benefit rodent MRI research community. Significant Methodological Contribution: We proposed a deep-learning-based framework to automatically identify the rodent brain boundaries in MRI. With a fully 3D convolutional network model, 3D U-Net, our proposed method demonstrated improved performance compared to current automatic brain extraction methods, as shown in several qualitative metrics (Dice, Jaccard, PPV, SEN, and Hausdorff). We trust that this tool will avoid human bias and streamline pre-processing steps during 3D high resolution rodent brain MRI data analysis. The software developed herein has been disseminated freely to the community.

Dataset Information

Fast, 3D Isotropic Imaging of Whole Mouse Brain Using Multiangle-Resolved Subvoxel SPIM.

Publications

Fast, 3D Isotropic Imaging of Whole Mouse Brain Using Multiangle-Resolved Subvoxel SPIM.

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