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Brain micro-vasculature imaging: An unsupervised deep learning algorithm for segmenting mouse brain volume probed by high-resolution phase-contrast X-ray tomography
International Journal of Imaging Systems and Technology ( IF 3.0 ) Pub Date : 2020-11-15 , DOI: 10.1002/ima.22520
Alessandra Patera 1 , Antonio G. Zippo 2 , Anne Bonnin 3 , Marco Stampanoni 3, 4 , Gabriele E. M. Biella 2
Affiliation  

High-throughput synchrotron-based tomographic microscopy at third generation light sources allows to probe cm-sized samples at micrometer-resolution. In this work, we present an approach to image a full mouse brain. With Indian-ink as a contrast agent, it was possible to obtain 3D distribution of microvessels while a computational framework automatically extracted the morphological and geometrical embedding of the putative vascular systems. Results demonstrate the potentiality of the proposed methodology to visualize and quantify in 3D details of the brain tissue with an image quality and resolution previously unachievable.

中文翻译:

脑微血管成像:一种用于分割由高分辨率相衬 X 射线断层扫描探测的小鼠脑体积的无监督深度学习算法

第三代光源下基于同步加速器的高通量断层显微术允许以微米分辨率探测厘米大小的样品。在这项工作中,我们提出了一种对完整小鼠大脑进行成像的方法。使用印度墨水作为造影剂,可以获得微血管的 3D 分布,同时计算框架自动提取假定血管系统的形态和几何嵌入。结果证明了所提出的方法具有可视化和量化脑组织的 3D 细节的潜力,其图像质量和分辨率是以前无法实现的。
更新日期:2020-11-15
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