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/商业潜力围绕着数字病理学中新兴的整个幻灯片成像市场细分市场。预计到2025年，数字病理市场将达到8亿美元以上。这个项目的动机来自于整个幻灯片图像(WSIS)的千兆字节大小，这是以接近光学分辨率产生的玻璃幻灯片的数字图像。随着医院和病理实验室产生的WSIS数量的迅速增加，WSIS的存储和管理已成为下一代图像分析迫切需要解决的问题。该项目的商业可行性可能会对希望使用现代集群计算和大数据技术管理大量信息社会世界首脑会议的研究人员、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)可以在大量的WSIS上更快地执行。该项目的潜在社会效益包括使病理学家能够使用下一代图像分析技术改进对疾病的诊断，以及创建一家带来新就业机会的科技初创公司。这个项目将为STEM中代表性不足群体的两名博士生提供培训。这个i-Corps项目基于一种软件技术，旨在解决WSIS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根本问题。这项技术的价值主张是高效和经济高效地存储大规模信息社会信息系统，并快速检索瓷砖，以实现用于人类疾病诊断的下一代图像分析。这项技术包括使用空间填充曲线的智能数据分区、内存中的数据结构以及有效的瓦片组织，以在图像分析期间实现瓦片的快速检索。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节点集群检索80个WSIS上的单个磁贴需要几秒钟时间。因此，我们相信，通过更快地访问图像块，基于WSIS的下一代图像分析(例如，使用深度学习)可以在大量WSIS上运行得更快，这可能会消耗TB的存储空间。由于该技术依赖于商用硬件和开源软件，因此性价比高，可以很容易地作为产品或服务进行部署。这项技术具有利用大数据方法推进WSI存储和管理的最先进水平的潜力。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势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.