Suppr超能文献

结合深度学习网络的对比增强型连续光学相干扫描仪揭示了小鼠大脑的脉管系统和白质结构。

Contrast-enhanced serial optical coherence scanner with deep learning network reveals vasculature and white matter organization of mouse brain.

作者信息

Li Tianqi, Liu Chao J, Akkin Taner

机构信息

University of Minnesota, Department of Biomedical Engineering, Minneapolis, Minnesota, United States.

出版信息

Neurophotonics. 2019 Jul;6(3):035004. doi: 10.1117/1.NPh.6.3.035004. Epub 2019 Jul 23.

Abstract

Optical coherence tomography provides volumetric reconstruction of brain structure with micrometer resolution. Gray matter and white matter can be highlighted using conventional and polarization-based contrasts; however, vasculature in fixed brain has not been investigated at large scale due to lack of intrinsic contrast. We present contrast enhancement to visualize the vasculature by perfusing titanium dioxide particles transcardially into the mouse vascular system. The brain, after dissection and fixation, is imaged by a serial optical coherence scanner. Accumulation of particles in blood vessels generates distinguishable optical signals. Among these, the cross-polarization images reveal the vasculature organization remarkably well. The conventional and polarization-based contrasts are still available for probing the gray matter and white matter structures. The segmentation and reconstruction of the vasculature are presented by using a deep learning algorithm. Axonal fiber pathways in the mouse brain are delineated by utilizing the retardance and optic axis orientation contrasts. This is a low-cost method that can be further developed to study neurovascular diseases and brain injury in animal models.

摘要

光学相干断层扫描能够以微米级分辨率对脑结构进行体积重建。使用传统的和基于偏振的对比方法可以突出灰质和白质;然而,由于缺乏内在对比,固定脑内的血管系统尚未得到大规模研究。我们通过经心将二氧化钛颗粒灌注到小鼠血管系统中来呈现对比增强,以可视化血管系统。在解剖和固定后,用连续光学相干扫描仪对脑进行成像。血管中颗粒的积累产生可区分的光信号。其中,交叉偏振图像能非常清晰地显示血管组织。传统的和基于偏振的对比方法仍可用于探测灰质和白质结构。通过使用深度学习算法对血管系统进行分割和重建。利用延迟和光轴方向对比来描绘小鼠脑中的轴突纤维通路。这是一种低成本方法,可进一步开发用于研究动物模型中的神经血管疾病和脑损伤。

相似文献

引用本文的文献

9
Machine learning analysis of whole mouse brain vasculature.机器学习分析全鼠脑血管结构
Nat Methods. 2020 Apr;17(4):442-449. doi: 10.1038/s41592-020-0792-1. Epub 2020 Mar 11.

本文引用的文献

1
Deep learning in imaging.成像中的深度学习。
Nat Methods. 2019 Jan;16(1):17. doi: 10.1038/s41592-018-0267-9.
7
CellProfiler 3.0: Next-generation image processing for biology.CellProfiler 3.0:生物学的下一代图像处理。
PLoS Biol. 2018 Jul 3;16(7):e2005970. doi: 10.1371/journal.pbio.2005970. eCollection 2018 Jul.
9
Deep learning for biology.用于生物学的深度学习
Nature. 2018 Feb 22;554(7693):555-557. doi: 10.1038/d41586-018-02174-z.

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验