多光谱图像
多路复用
工作流程
计算机科学
计算生物学
感知
人工智能
可视化
模式识别(心理学)
病理
计算机视觉
生物信息学
医学
生物
神经科学
数据库
作者
Edward C. Stack,Chichung Wang,Kristin Roman,Clifford Hoyt
出处
期刊:Methods
[Elsevier BV]
日期:2014-09-19
卷期号:70 (1): 46-58
被引量:656
标识
DOI:10.1016/j.ymeth.2014.08.016
摘要
Tissue sections offer the opportunity to understand a patient’s condition, to make better prognostic evaluations and to select optimum treatments, as evidenced by the place pathology holds today in clinical practice. Yet, there is a wealth of information locked up in a tissue section that is only partially accessed, due mainly to the limitations of tools and methods. Often tissues are assessed primarily based on visual analysis of one or two proteins, or 2–3 DNA or RNA molecules. Even while analysis is still based on visual perception, image analysis is starting to address the variability of human perception. This is in contrast to measuring characteristics that are substantially out of reach of human perception, such as parameters revealed through co-expression, spatial relationships, heterogeneity, and low abundance molecules. What is not routinely accessed is the information revealed through simultaneous detection of multiple markers, the spatial relationships among cells and tissue in disease, and the heterogeneity now understood to be critical to developing effective therapeutic strategies. Our purpose here is to review and assess methods for multiplexed, quantitative, image analysis based approaches, using new multicolor immunohistochemistry methods, automated multispectral slide imaging, and advanced trainable pattern recognition software. A key aspect of our approach is presenting imagery in a workflow that engages the pathologist to utilize the strengths of human perception and judgment, while significantly expanding the range of metrics collectable from tissue sections and also provide a level of consistency and precision needed to support the complexities of personalized medicine.
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