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 300×, 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.

中文翻译：

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利用多级并行性拼接超大显微镜图像

由于显微镜的视野有限，宏观标本的采集需要许多平行的图像堆栈来覆盖整个感兴趣的体积。在堆栈之间引入重叠区域，以便通过 3D 拼接工具进行自动对齐。由于最先进的显微镜加上化学清除程序可以生成大小超过 TB 的 3D 图像，因此需要并行化以将拼接时间保持在可接受的范围内。在本文中，我们讨论了多级并行化如何减少 TeraStitcher 的执行时间，TeraStitcher 是一种旨在处理超大图像的工具。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以及实验结果证明了该方法的有效性，当同时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，无需更改用户界面，也无需额外的代码维护工作。