Large-scale storage of whole slide images and fast retrieval of tiles using DRAM
Large-scale storage of whole slide images and fast retrieval of tiles using DRAM
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DOI：
10.1117/12.2564694
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发表时间：
2020-04
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通讯作者：
Daniel E. Lopez Barron;P. Rao;D. Rao;O. Tawfik;Arun Zachariah
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作者：
Daniel E. Lopez Barron;P. Rao;D. Rao;O. Tawfik;Arun Zachariah
美国食品和药物管理局(FDA)已经批准了两个数字病理系统用于初步诊断。这些系统使用先进的数字载玻片扫描仪制作和使用由玻璃载玻片构成的完整载玻片图像(WSIS)。通过开发新的图像分析软件，WSIS可以极大地提高病理学家的工作水平，该软件用于自动检测细胞和形态特征，并使用组织病理学切片进行疾病诊断。然而，WSI的千兆字节大小对数百万WSI的存储和检索构成了严重挑战。在本文中，我们提出了一个可伸缩存储的WSIS和使用DRAM快速检索图像分片的系统。使用空间填充曲线、递归分区和杜威编号的组合将WSI划分为平铺和子平铺。然后，它们作为键-值对的集合存储在DRAM中。在检索过程中，使用键值查找从DRAM中提取平铺。通过使用100个WSIS在24节点群集上进行性能评估，我们观察到，与Apache Spark相比，我们的系统存储100个WSIS的速度快3倍，访问单个磁贴的速度快1000倍，实现了毫秒级的延迟。在数百万个信息社会信息系统上开发基于深度学习的图像分析解决方案时，这种快速访问磁贴是非常理想的。
The U.S. Food and Drug Administration (FDA) has approved two digital pathology systems for primary diagnosis. These systems produce and consume whole slide images (WSIs) constructed from glass slides using advanced digital slide scanners. WSIs can greatly improve the work ow of pathologists through the development of novel image analytics software for automatic detection of cellular and morphological features and disease diagnosis using histopathology slides. However, the gigabyte size of a WSI poses a serious challenge for storage and retrieval of millions of WSIs. In this paper, we propose a system for scalable storage of WSIs and fast retrieval of image tiles using DRAM. A WSI is partitioned into tiles and sub-tiles using a combination of a space-filling curve, recursive partitioning, and Dewey numbering. They are then stored as a collection of key-value pairs in DRAM. During retrieval, a tile is fetched using key-value lookups from DRAM. Through performance evaluation on a 24-node cluster using 100 WSIs, we observed that, compared to Apache Spark, our system was three times faster to store the 100 WSIs and 1,000 times faster to access a single tile achieving millisecond latency. Such fast access to tiles is highly desirable when developing deep learning-based image analytics solutions on millions of WSIs.