Development and Dissemination of MuscleMiner: An Imaging Informatics Tool for Mus

MuscleMiner 的开发和传播：Mus 的成像信息学工具

• 批准号: 8761698
• 负责人: Lin Yang
• 金额: $ 31.43万
• 依托单位: UNIVERSITY OF FLORIDA
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-08 至 2019-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8761698
• 关键词: Address; Adoption; Architecture; Archives; Area; Award; Basic Science; Big Data; Bioinformatics; Biological; Biopsy; Caliber; Cells; Client; Cloud Computing; Collection; Communities; Computer software; Computers; Data; Data Analyses; Development; Diagnosis; Diagnostic; Disease; Exhibits; Fascicle; Fiber; Goals; Health; Image; Image Analysis; Imagery; Informatics; Institution; Interobserver Variability; Label; Laboratories; Lead; Licensing; Location; Machine Learning; Manuals; Measurement; Measures; Medicine; Methods; Mining; Mus; Phase; Procedures; Process; Research; Research Personnel; Rest; Retrieval; Shapes; Slide; Small Business Technology Transfer Research; System; Technology; United States National Institutes of Health; Validation; Visual; Work; base; bioimaging; biomedical informatics; clinical practice; computerized data processing; cost; design; frontier; image archival system; image visualization; imaging informatics; improved; indexing; novel; open source; outcome forecast; prognostic; public health relevance; skeletal; tool; wasting

DESCRIPTION (provided by applicant): Image evaluation of skeletal muscle biopsies is a procedure essential to research and clinical practice. Although widely used, several major limitations exist with respect to current muscle image morphometric measurement, archiving, visualization, querying, searching, retrieval, and mining procedures: 1) Although traditional morphometric parameters, such as cross-sectional area (CSA) and minimum Feret diameter, etc., serve as critical indicators for assessing muscle function, current measurements are still largely based on manual or semi-automated methods, leading to significant labor costs with large potential inter-observer variability. 2) The current archiving of muscle images is still mainy based on outdated tools such as Excel spreadsheets and computer file folders. Given a new muscle image, it is almost impossible to quickly cross-compare, visualize, query, search, and retrieve previous cases exhibiting similar image contents with comparable morphometric measures, for the purpose of either discovering novel biological co-correlations at benchside, or providing personalized diagnosis and prognosis at bedside. 3) Although a typical muscle image often contains millions of data points (pixels), in clinical practice, doctors often condense this rich information into one or two diagnostic labels and discard the rest. Novel image markers, which are not always apparent through visual inspections or not manually quantifiable, but potentially represent critical diagnostic and prognostic values for precision medicine, have not been rigorously examined. Similarly, in basic science research, only a very limited number of known measures (e.g., CSA) are considered. Some non-traditional measures, such as myofiber shapes that hold the potential to serve as new indicators of muscle functions, are not fully investigated. 4) Current muscle image analysis and searching functions are fairly low throughput. As a frontier research area, Cloud computing can handle big image data in a distributed manner by providing high-throughput computational power. However, its application to muscle images has never been explored. MuscleMiner will provide a complete suite of tools for automated image morphometric measurements, archiving, visualization, querying, searching, content-based image retrieval, bioinformatics image mining, and Cloud computing. The objectives of this proposal are to: 1) Develop the automated morphometric measurement unit, content-based image retrieval (CBIR) unit, and the image archiving and visualization unit. 2) Develop the advanced bioinformatics image mining unit to assist in the rapid discovery and validation of new image markers. 3) Develop the Cloud computing unit to enable big image data processing and searching functions. Disseminate this freely available, Cloud-enabled imaging informatics system to muscle research community.

描述（由申请人提供）：骨骼肌活检的图像评价是研究和临床实践中必不可少的程序。尽管被广泛使用，但是关于当前肌肉图像形态测量、存档、可视化、查询、搜索、检索和挖掘过程存在几个主要局限性：1）虽然传统的形态测量参数，例如横截面积（CSA）和最小Feret直径等，作为评估肌肉功能的关键指标，目前的测量仍然主要基于手动或半自动化方法，导致显著的劳动力成本和潜在的观察者间差异。2)目前肌肉图像的存档仍然主要基于过时的工具，如Excel电子表格和计算机文件夹。给定新的肌肉图像，几乎不可能快速交叉比较、可视化、查询、搜索和检索先前的病例，这些病例表现出具有可比形态测量的相似图像内容，目的是在临床上发现新的生物学相关性，或者在临床上提供个性化诊断和预后。3)尽管一张典型的肌肉图像通常包含数百万个数据点（像素），但在临床实践中，医生往往会将这些丰富的信息浓缩到一两个诊断标签中，而丢弃其余的。新的图像标记物并不总是通过目视检查或手动量化而明显，但可能代表精确医学的关键诊断和预后价值，尚未进行严格检查。同样，在基础科学研究中，只有非常有限数量的已知措施（例如，CSA）。一些非传统的措施，如肌纤维的形状，有可能作为肌肉功能的新指标，没有得到充分的研究。4)当前的肌肉图像分析和搜索功能的吞吐量相当低。云计算作为一个前沿研究领域，能够提供高吞吐量的计算能力，以分布式的方式处理海量图像数据。然而，它的肌肉图像的应用从来没有被探索过。MuscleMiner将提供一套完整的工具，用于自动图像形态测量，存档，可视化，查询，搜索，基于内容的图像检索，生物信息学图像挖掘和云计算。本计画的目标是：1）发展自动化的形态测量单元、以内容为基础的影像撷取（CBIR）单元、以及影像存档与视觉化单元。2)开发先进的生物信息学图像挖掘单元，帮助快速发现和验证新的图像标记。3)开发云计算单元，实现大图像数据处理和搜索功能。将这个免费提供的云成像信息系统传播给肌肉研究社区。