Microscopic imaging is an important tool for characterizing tissue morphology and pathology. 3D reconstruction and visualization of large sample tissue structure requires registration of large sets of high-resolution images. However, the scale of this problem presents a challenge for automatic registration methods. In this paper we present a novel method for efficient automatic registration using graphics processing units (GPUs) and parallel programming. Comparing a C++ CPU implementation with Compute Unified Device Architecture (CUDA) libraries and pthreads running on GPU we achieve a speed-up factor of up to 4.11× with a single GPU and 6.68× with a GPU pair. We present execution times for a benchmark composed of two sets of large-scale images: mouse placenta (16K × 16K pixels) and breast cancer tumors (23K × 62K pixels). It takes more than 12 hours for the genetic case in C++ to register a typical sample composed of 500 consecutive slides, which was reduced to less than 2 hours using two GPUs, in addition to a very promising scalability for extending those gains easily on a large number of GPUs in a distributed system.

中文翻译：

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图形处理器上大量显微图像的非刚性配准。

显微成像是表征组织形态学和病理学的重要工具。大样本组织结构的 3D 重建和可视化需要对大量高分辨率图像进行配准。然而，这个问题的规模对自动配准方法提出了挑战。在本文中，我们提出了一种使用图形处理单元 (GPU) 和并行编程进行高效自动配准的新方法。将 C++ CPU 实现与计算统一设备架构 (CUDA) 库和在 GPU 上运行的 pthread 进行比较，我们实现了单 GPU 高达 4.11 倍和一对 GPU 高达 6.68 倍的加速因子。我们展示了由两组大型图像组成的基准测试的执行时间：小鼠胎盘（16K × 16K 像素）和乳腺癌肿瘤（23K × 62K 像素）。