用于测量斑马鱼大脑白质体积的厚共聚焦显微镜3D图像的自动分割

Sylvain Lempereur, Arnim Jenett, Elodie Machado, Ignacio Arganda-Carreras, Matthieu Simion, P. Affaticati, J. Joly, Hugues Talbot
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引用次数: 2

摘要

组织清除方法促进了厚样品的显微观察,如全贴装小鼠或斑马鱼。即使使用最好的组织清除方法,标本也不是完全透明的,光衰减随深度增加,降低了信号输出和信噪比。此外,由于组织清除和显微采集技术变得更快,自动图像分析现在是一个问题。在这种情况下,大规模安装样品通常会导致样品不完全对齐或定向,这使得依赖于预定义的、与样品无关的参数来纠正信号衰减是不可能的。本文提出了一种基于样本的对比度校正方法。它依赖于分割样本,并估计样本深度等值面,作为校正的参考。我们将斑马鱼幼体的脑白质进行分割。我们发现,这种校正可以更好地拼接每个幼虫的相对侧面,以便在整个过程中以高信噪比对整个幼虫进行成像。我们还表明,我们提出的对比度校正方法可以通过比较手动和自动分割来更好地识别大脑的深层结构。这有望改善图像观察和分析在高含量的方法中,在样品中的信号损失是显著的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Automated segmentation of thick confocal microscopy 3D images for the measurement of white matter volumes in zebrafish brains
Abstract Tissue clearing methods have boosted the microscopic observations of thick samples such as whole-mount mouse or zebrafish. Even with the best tissue clearing methods, specimens are not completely transparent and light attenuation increases with depth, reducing signal output and signal-to-noise ratio. In addition, since tissue clearing and microscopic acquisition techniques have become faster, automated image analysis is now an issue. In this context, mounting specimens at large scale often leads to imperfectly aligned or oriented samples, which makes relying on predefined, sample-independent parameters to correct signal attenuation impossible. Here, we propose a sample-dependent method for contrast correction. It relies on segmenting the sample, and estimating sample depth isosurfaces that serve as reference for the correction. We segment the brain white matter of zebrafish larvae. We show that this correction allows a better stitching of opposite sides of each larva, in order to image the entire larva with a high signal-to-noise ratio throughout. We also show that our proposed contrast correction method makes it possible to better recognize the deep structures of the brain by comparing manual vs. automated segmentations. This is expected to improve image observations and analyses in high-content methods where signal loss in the samples is significant.
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