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基于图像的连续和不连续非平面轴向畸变在连续切片显微镜中的校正。

Image-based correction of continuous and discontinuous non-planar axial distortion in serial section microscopy.

作者信息

Hanslovsky Philipp, Bogovic John A, Saalfeld Stephan

机构信息

HHMI Janelia Research Campus, Ashburn, VA 20147, USA.

出版信息

Bioinformatics. 2017 May 1;33(9):1379-1386. doi: 10.1093/bioinformatics/btw794.

Abstract

MOTIVATION

Serial section microscopy is an established method for detailed anatomy reconstruction of biological specimen. During the last decade, high resolution electron microscopy (EM) of serial sections has become the de-facto standard for reconstruction of neural connectivity at ever increasing scales (EM connectomics). In serial section microscopy, the axial dimension of the volume is sampled by physically removing thin sections from the embedded specimen and subsequently imaging either the block-face or the section series. This process has limited precision leading to inhomogeneous non-planar sampling of the axial dimension of the volume which, in turn, results in distorted image volumes. This includes that section series may be collected and imaged in unknown order.

RESULTS

We developed methods to identify and correct these distortions through image-based signal analysis without any additional physical apparatus or measurements. We demonstrate the efficacy of our methods in proof of principle experiments and application to real world problems.

AVAILABILITY AND IMPLEMENTATION

We made our work available as libraries for the ImageJ distribution Fiji and for deployment in a high performance parallel computing environment. Our sources are open and available at http://github.com/saalfeldlab/section-sort, http://github.com/saalfeldlab/z-spacing and http://github.com/saalfeldlab/z-spacing-spark.

CONTACT

saalfelds@janelia.hhmi.org.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

摘要

动机

连续切片显微镜技术是一种用于生物样本详细解剖结构重建的既定方法。在过去十年中,连续切片的高分辨率电子显微镜(EM)已成为在不断扩大的尺度上重建神经连接性(EM连接组学)的事实上的标准方法。在连续切片显微镜技术中,通过从包埋好的样本中物理移除薄片,然后对块面或切片系列进行成像,来对体积的轴向维度进行采样。这个过程精度有限,导致对体积轴向维度的采样不均匀且非平面,进而导致图像体积失真。这包括切片系列可能会以未知顺序收集和成像。

结果

我们开发了通过基于图像的信号分析来识别和纠正这些失真的方法,无需任何额外的物理设备或测量。我们在原理验证实验以及对实际问题的应用中证明了我们方法的有效性。

可用性和实现方式

我们将我们的工作作为库提供给ImageJ发行版Fiji,并用于在高性能并行计算环境中部署。我们的源代码是开放的,可在http://github.com/saalfeldlab/section-sort、http://github.com/saalfeldlab/z-spacing和http://github.com/saalfeldlab/z-spacing-spark上获取。

联系方式

saalfelds@janelia.hhmi.org

补充信息

补充数据可在《生物信息学》在线获取。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2a84/5860381/02580d9ea71c/btw794f1.jpg

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