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BENCHMARKING OF ALGORITHMS FOR 3D TISSUE RECONSTRUCTION

机译：3D组织重建算法的基准

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摘要

Studying tissue structure in 3D is beneficial in many applications. Reconstructing the structure based on histological sections has the advantages of high resolution and compatibility with conventional staining and interpretation techniques. However, obtaining an accurate 3D reconstruction based on a sequence of 2D sections is a difficult task. Evaluating the accuracy of such reconstructions is also challenging and it is often performed based only on visual inspections or a single indirect numerical measure. Here, we present a benchmarking framework composed of a panel of complementary metrics for assessing the quality of 3D reconstructions. We then apply the framework to evaluate the performance of several popular image registration algorithms in this context.
机译：在许多应用中，研究3D中的组织结构是有益的。基于组织学部分重建结构具有高分辨率和与传统染色和解释技术的兼容性的优点。然而，基于2D部分的序列获得精确的3D重建是困难的任务。评估这种重建的准确性也是具有挑战性的，并且通常仅基于目视检查或单一间接数值来执行。在这里，我们介绍由互补度量小组组成的基准框架，用于评估3D重建的质量。然后，我们将框架应用于在此上下文中评估几个流行的图像配准算法的性能。

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