Exploiting Multi-Level Parallelism for Stitching Very Large Microscopy Images
Exploiting Multi-Level Parallelism for Stitching Very Large Microscopy Images
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DOI：
10.3389/fninf.2019.00041
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发表时间：
2019-06-04
影响因子：
3.5
通讯作者：
Iannello, Giulio
中科院分区：
文献类型：
--
作者：
Bria, Alessandro;Bernaschi, Massimo;Iannello, Giulio
由于显微镜的视野有限，宏观标本的采集需要许多并行图像堆栈来覆盖整个感兴趣的体积。在堆叠之间引入重叠区域，以便可以通过 3D 拼接工具自动对齐。由于最先进的显微镜与化学清除程序相结合可以生成大小超过太字节的 3D 图像，因此需要并行化才能将拼接时间保持在可接受的限度内。在本文中，我们讨论多级并行化如何减少 TeraStitcher（一种设计用于处理非常大图像的工具）的执行时间。提出了两种以透明方式执行数据集分区以实现高效并行化的算法，并提供了实验结果，证明了该方法的有效性，当利用粗粒度和细粒度并行性时，该方法可实现接近 300 倍的加速。 TeraStitcher 的多级并行化显着减少了处理时间，而无需更改用户界面，也不需要额外的代码维护工作。
Due to the limited field of view of the microscopes, acquisitions of macroscopic specimens require many parallel image stacks to cover the whole volume of interest. Overlapping regions are introduced among stacks in order to make it possible automatic alignment by means of a 3D stitching tool. Since state-of-the-art microscopes coupled with chemical clearing procedures can generate 3D images whose size exceeds the Terabyte, parallelization is required to keep stitching time within acceptable limits. In the present paper we discuss how multi-level parallelization reduces the execution times of TeraStitcher, a tool designed to deal with very large images. Two algorithms performing dataset partition for efficient parallelization in a transparent way are presented together with experimental results proving the effectiveness of the approach that achieves a speedup close to 300x, when both coarse- and fine-grained parallelism are exploited. Multi-level parallelization of TeraStitcher led to a significant reduction of processing times with no changes in the user interface, and with no additional effort required for the maintenance of code.