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一种用于无参考三维组织学图像重建的高斯-赛德尔迭代方案。

A Gauss-Seidel iteration scheme for reference-free 3-D histological image reconstruction.

作者信息

Gaffling Simone, Daum Volker, Steidl Stefan, Maier Andreas, Kostler Harald, Hornegger Joachim

出版信息

IEEE Trans Med Imaging. 2015 Feb;34(2):514-30. doi: 10.1109/TMI.2014.2361784. Epub 2014 Oct 8.

DOI:10.1109/TMI.2014.2361784
PMID:25312918
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC4418037/
Abstract

Three-dimensional (3-D) reconstruction of histological slice sequences offers great benefits in the investigation of different morphologies. It features very high-resolution which is still unmatched by in vivo 3-D imaging modalities, and tissue staining further enhances visibility and contrast. One important step during reconstruction is the reversal of slice deformations introduced during histological slice preparation, a process also called image unwarping. Most methods use an external reference, or rely on conservative stopping criteria during the unwarping optimization to prevent straightening of naturally curved morphology. Our approach shows that the problem of unwarping is based on the superposition of low-frequency anatomy and high-frequency errors. We present an iterative scheme that transfers the ideas of the Gauss-Seidel method to image stacks to separate the anatomy from the deformation. In particular, the scheme is universally applicable without restriction to a specific unwarping method, and uses no external reference. The deformation artifacts are effectively reduced in the resulting histology volumes, while the natural curvature of the anatomy is preserved. The validity of our method is shown on synthetic data, simulated histology data using a CT data set and real histology data. In the case of the simulated histology where the ground truth was known, the mean Target Registration Error (TRE) between the unwarped and original volume could be reduced to less than 1 pixel on average after six iterations of our proposed method.

摘要

组织学切片序列的三维(3-D)重建在不同形态学研究中具有很大优势。它具有非常高的分辨率,这仍然是体内3-D成像方式无法比拟的,并且组织染色进一步提高了可见性和对比度。重建过程中的一个重要步骤是反转组织学切片制备过程中引入的切片变形,这个过程也称为图像去扭曲。大多数方法使用外部参考,或者在去扭曲优化过程中依赖保守的停止标准来防止自然弯曲形态的拉直。我们的方法表明,去扭曲问题基于低频解剖结构和高频误差的叠加。我们提出了一种迭代方案,将高斯-赛德尔方法的思想应用于图像堆栈,以将解剖结构与变形分离。特别是,该方案普遍适用,不受特定去扭曲方法的限制,并且不使用外部参考。在所得的组织学体积中,变形伪影得到有效减少,同时解剖结构的自然曲率得以保留。我们的方法在合成数据、使用CT数据集的模拟组织学数据和真实组织学数据上都显示了有效性。在已知真实情况的模拟组织学案例中,经过我们提出的方法六次迭代后,去扭曲体积与原始体积之间的平均目标配准误差(TRE)平均可降至小于1像素。

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