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中新兴的整个载玻片成像市场。数字病理学市场预计到2025年将达到8亿美元以上。该项目的动机源于全载玻片图像（WSI）的千兆字节大小，这些图像是以近光学分辨率制作的载玻片的数字图像。随着医院和病理实验室产生的WSI数量的快速增加，WSI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以显著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，下一代图像分析（例如，使用深度学习）自动检测和分析人体组织中的细胞和形态特征，可以在大量WSI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来改进疾病诊断，以及创建一家技术创业公司，从而创造新的就业机会。该项目将为两名博士提供培训。这个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瓦片的基本问题。该技术的价值主张是大规模WSI的高效和经济高效的存储以及切片的快速检索，从而为人类疾病诊断提供下一代图像分析。该技术包括使用空间填充曲线的智能数据分区、内存中的数据结构和有效的切片组织，以实现在图像分析期间快速检索切片。它采用节省空间的存储格式，以最大限度地提高存储效率。平均而言，使用16个节点的群集在80个WSI上检索单个切片需要几秒钟。因此，我们相信基于WSI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切片，在大量的WSI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，因此具有成本效益，并且可以轻松地部署为产品或服务。该技术有可能使用大数据方法推进WSI存储和管理的最新技术水平。该奖项反映了NSF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.