I-Corps: Scalable Storage of Whole Slide Images and Fast Retrieval of Tiles for Next-Generation Image Analytics

I-Corps：整个幻灯片图像的可扩展存储和用于下一代图像分析的图块的快速检索

• 批准号: 2024429
• 负责人: Praveen Rao
• 金额: $ 1.62万
• 依托单位: University of Missouri-Columbia
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-02-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2024429&HistoricalAwards=false
• 关键词: Corps; Scalable; Storage; Whole; Slide

The broader impact/commercial potential of this I-Corps project is pivoted around the emerging whole slide imaging market segment in digital pathology. The digital pathology market is predicted to reach over $800 million by 2025. The motivation of this project stems from the gigabyte size of whole slide images (WSIs), which are digital images of glass slides produced at near optical resolution. With rapid increase in the number of WSIs produced by hospitals and pathology labs, the storage and management of WSIs has become an urgent problem to tackle for next-generation image analytics. The commercial viability of the project can significantly impact researchers, medical professionals, software developers, and IT staff who wish to manage large number of WSIs using modern cluster computing and big data techniques. Thus, next-generation image analytics (e.g., using deep learning) for automatic detection and analysis of cellular and morphological features in human tissues can be performed faster on large numbers of WSIs. The potential societal benefit of the project includes enabling improved diagnosis of diseases by pathologists using next-generation image analytics and the creation of a tech startup leading to new jobs. This project will provide training to two Ph.D. students from underrepresented groups in STEM.This I-Corps project is based on a software technology that aims to solve the fundamental problem of scalable storage of WSIs and fast retrieval of tiles using a commodity cluster and big data techniques. The value proposition of the technology is efficient and cost-effective storage of large-scale WSIs and fast retrieval of tiles to enable next-generation image analytics for human disease diagnosis. The technology encompasses intelligent data partitioning using space-filling curves, in-memory data structures, and effective organization of tiles to enable fast retrieval of tiles during image analysis. It employs space-efficient storage formats to maximize storage efficiency. On an average, it required a few seconds to retrieve a single tile on 80 WSIs using a 16-node cluster. Therefore, we believe next-generation image analytics on WSIs (e.g., using deep learning) can run faster on large number of WSIs, which can consume terabytes of storage, through faster access of image tiles. As the technology relies on commodity hardware and open source software, it is cost-effective and can be easily deployed as a product or a service. The technology has the potential to advance the state-of-the-art in WSI storage and management using a big data approach.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该 I-Corps 项目的更广泛影响/商业潜力以数字病理学中新兴的整个幻灯片成像市场为中心。预计到 2025 年，数字病理学市场将达到 8 亿美元以上。该项目的动机源于千兆字节大小的整个载玻片图像 (WSI)，这是以接近光学分辨率生成的玻璃载玻片的数字图像。随着医院和病理实验室产生的 WSI 数量迅速增加，WSI 的存储和管理已成为下一代图像分析亟待解决的问题。该项目的商业可行性可以极大地影响希望使用现代集群计算和大数据技术管理大量 WSI 的研究人员、医疗专业人员、软件开发人员和 IT 人员。因此，用于自动检测和分析人体组织中的细胞和形态特征的下一代图像分析（例如，使用深度学习）可以在大量 WSI 上更快地执行。该项目的潜在社会效益包括病理学家使用下一代图像分析技术改进疾病诊断，以及创建一家科技初创公司以创造新的就业机会。该项目将为两名博士提供培训。来自 STEM 中代表性不足群体的学生。这个 I-Corps 项目基于软件技术，旨在解决 WSI 可扩展存储和使用商品集群和大数据技术快速检索图块的基本问题。该技术的价值主张是高效且经济高效地存储大规模 WSI 以及快速检索图块，从而实现用于人类疾病诊断的下一代图像分析。该技术包括使用空间填充曲线、内存中数据结构和有效组织图块的智能数据分区，以便在图像分析过程中快速检索图块。它采用节省空间的存储格式来最大限度地提高存储效率。平均而言，使用 16 节点集群在 80 个 WSI 上检索单个图块需要几秒钟的时间。因此，我们相信 WSI 上的下一代图像分析（例如，使用深度学习）可以通过更快地访问图像图块，在大量 WSI 上运行得更快，这些 WSI 可能会消耗 TB 的存储空间。由于该技术依赖于商用硬件和开源软件，因此具有成本效益，并且可以轻松部署为产品或服务。该技术有潜力利用大数据方法推进 WSI 存储和管理的最先进水平。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优点和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Large-scale storage of whole slide images and fast retrieval of tiles using DRAM

• DOI: 10.1117/12.2564694
• 发表时间: 2020-04
• 作者: Daniel E. Lopez Barron;P. Rao;D. Rao;O. Tawfik;Arun Zachariah