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Image-Based Informatics for Preclinical Biomedical Research

机译:基于图像的临床前生物医学研究信息

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In 2006, the New England Journal of Medicine selected medical imaging as one of the eleven most important innovations of the past 1,000 years, primarily due to its ability to allow physicians and researchers to visualize the very nature of disease. As a result of the broad-based adoption of micro imaging technologies, preclinical researchers today are generating terabytes of image data from both anatomic and functional imaging modes. In this paper we describe our early research to apply content-based image retrieval to index and manage large image libraries generated in the study of amyloid disease in mice. Amyloidosis is associated with diseases such as Alzheimer's, type 2 diabetes, chronic inflammation and myeloma. In particular, we will focus on results to date in the area of small animal organ segmentation and description for CT, SPECT, and PET modes and present a small set of preliminary retrieval results for a specific disease state in kidney CT cross-sections.
机译:2006年,新英格兰医学杂志选择医学成像作为过去1000年的十一最重要的创新之一,主要是由于其允许医生和研究人员可视化疾病本质的能力。由于基于广泛的微影像技术采用,目前的临界研究人员正在从两个解剖学和功能成像模式产生图像数据的TB。在本文中,我们描述了我们早期的研究,以将基于内容的图像检索应用于索引和管理小鼠淀粉样疾病研究中产生的大型图像文库。淀粉样变性与阿尔茨海默,2型糖尿病,慢性炎症和骨髓瘤等疾病有关。特别是,我们将关注在小动物器官细分和CT,SPECT和PET模式的描述中的结果,并对肾CT横截面的特定疾病状态提出一小一组初步检索结果。

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