Task-Specific Compression for Biomedical Big Data

 描述（由申请人提供）：当代生物医学研究越来越多地产生和使用非常大的数据集。随着生物医学大数据的收集、处理和存储，一个关键的挑战是有效地维护和交付这些数据。与大数据相关的挑战在数字病理学中尤为突出：单个数字全载玻片图像（WSI）需要2到10 GB的文件大小。当考虑整个病例（通常在4到30种不同的染色剂之间）时，原始数据大小可能超过100 GB。随着在不同焦平面（z堆栈）成像和多光谱成像变得可用，预期来自单个病例的原始数据在不久的将来将达到几个TB也不是不合理的。一个典型的学术病理学部门的载玻片量需要多个扫描仪的全天候操作，这些扫描仪可以装载数百个载玻片并可以连续扫描。因此，预期由完全数字化病理学实践生成的数据量是巨大的。 该提案的目的是解决 这个具有挑战性的问题。我们的假设是，它是可能的，以显着改善数字病理图像的准确诊断，通过设计智能图像压缩方案的介绍。我们提出了三个具体目标来测试这一假设：目标1：开发和验证有效的和智能的图像压缩技术优化的基础上人类视觉系统（HVS）的属性。这些技术将最佳地调整压缩参数，使得针对每一个图像实现期望的视觉质量。 目标二：开发和验证一种新的图像压缩范例，其中与手头任务最相关的信息被优先存储和传输。我们建议使用特定任务的信息（TSI）作为压缩过程中的图像保真度的度量。 目标3：开发一个客户端-服务器框架，允许通过带宽有限的网络交互式远程浏览非常高分辨率的病理学图像。

 DESCRIPTION (provided by applicant): Contemporary biomedical research increasingly generates and uses very large datasets. As this seemingly unending supply of biomedical Big Data is collected, processed, and stored, a key challenge is maintaining and delivering this data efficiently. The challenges associated with Big Data are particularly prominent in digital pathology: A single digital whole slide image (WSI) requires a file size in the 2 to 10GB range. When an entire case is considered (typically between 4 to 30 different stains), the raw data size can exceed 100 GB. With imaging at different focal planes (z-stacks) and multispectral imaging becoming available, it is not unreasonable to expect that the raw data from a single case will reach several TBs in the near future. The slide volumes of a typical academic pathology department require round-the-clock operation of multiple scanners which can be loaded with hundreds of slides and can scan continuously. Thus, the volume of data that is expected to be generated by a fully digital pathology practice is enormous. The goal of this proposal is to solve this challenging problem. Our hypothesis is that it is possible to significantly improve the presentation of digital pathology images for accurate diagnoses by designing intelligent image compression schemes. We propose three Specific Aims to test this hypothesis: Aim 1: To develop and validate efficient and intelligent image compression techniques optimized based on the properties of the Human Visual System (HVS). These techniques will optimally tune the compression parameters such that desired visual quality is achieved for each and every image. Aim 2: To develop and validate a novel image compression paradigm where information which is most relevant to the task at hand is stored and transmitted preferentially. We propose to use Task-Specific Information (TSI) as a metric of image fidelity during compression. Aim 3: To develop a client-server framework that will allow interactive remote browsing of very high resolution pathology images over bandwidth-limited networks.