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

这个I-Corps项目的更广泛的影响/商业潜力是围绕数字病理学新兴的整个幻灯片成像市场细分。到2025年，数字病理学市场预计将超过8亿美元。这个项目的动机源于整片幻灯片图像（wsi）的千兆字节大小，这是在接近光学分辨率下产生的玻璃幻灯片的数字图像。随着医院和病理实验室生产的wsi数量的迅速增加，wsi的存储和管理已成为下一代图像分析急需解决的问题。该项目的商业可行性可以对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人员产生重大影响。因此，用于自动检测和分析人体组织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：病理学家利用下一代图像分析技术提高疾病诊断水平，并创建一家科技创业公司，创造新的就业机会。该项目将为两名来自STEM领域代表性不足群体的博士生提供培训。I-Corps项目基于一种软件技术，旨在解决wsi的可扩展存储和使用商品集群和大数据技术快速检索瓷砖的基本问题。该技术的价值主张是高效且经济地存储大规模wsi和快速检索图像，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、内存数据结构和有效组织块的智能数据分区，以便在图像分析期间快速检索块。它采用空间高效的存储格式，以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tile需要几秒钟的时间。因此，我们相信下一代wsi上的图像分析（例如，使用深度学习）可以通过更快地访问图像块，在大量wsi上运行得更快，这可能会消耗数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，并且可以很容易地作为产品或服务部署。该技术具有利用大数据方法推进WSI存储和管理的潜力。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.