When dealing with giga-pixel digital pathology in whole-slide imaging, a
notable proportion of data records holds relevance during each analysis
operation. For instance, when deploying an image analysis algorithm on
whole-slide images (WSI), the computational bottleneck often lies in the
input-output (I/O) system. This is particularly notable as patch-level
processing introduces a considerable I/O load onto the computer system.
However, this data management process can be potentially further paralleled,
given the typical independence of patch-level image processes across different
patches. This paper details our endeavors in tackling this data access
challenge through the implementation of the Adaptable IO System version 2
(ADIOS2). Our focus has been on constructing and releasing a digital
pathology-centric pipeline using ADIOS2, which facilitates streamlined data
management across WSIs. Additionally, we've developed strategies aimed at
curtailing data retrieval times. The performance evaluation encompasses two key
scenarios: (1) a pure CPU-based image analysis scenario (termed the "CPU
scenario"), and (2) a GPU-based deep learning framework scenario (referred to
as the "GPU scenario"). Our findings reveal noteworthy outcomes. Under the CPU
scenario, ADIOS2 showcases an impressive two-fold speed-up in comparison to the
brute-force approach. In the GPU scenario, its performance stands on par with
the cutting-edge GPU I/O acceleration framework, NVIDIA Magnum IO GPU Direct
Storage (GDS). From what we know, this appears to be among the initial
instances, if any, of utilizing ADIOS2 within the field of digital pathology.
The source code has been made publicly available at
this https URL

在处理千兆像素数字病理学整张切片图像时，我们通过实施适应性 IO 系统版本 2（ADIOS2）来解决数据访问挑战，构建和发布了一个以数字病理学为中心的流水线，实现了切片图像数据的高效管理，并开发了旨在缩短数据检索时间的策略。我们的研究结果显示，在 CPU 方案中，ADIOS2 的速度比传统方法提升了两倍；在 GPU 方案中，其性能与领先的 GPU I/O 加速框架 NVIDIA Magnum IO GPU Direct Storage（GDS）相当。根据我们所知，这可能是在数字病理学领域内首次使用 ADIOS2。