数字病理学:我们离基于组织的自动诊断还有多远?

K. Kayser, S. Borkenfeld, A. Djenouni, Joachim Christian Manning, H. Kaltner, G. Kayser, H. Gabius
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引用次数: 2

摘要

基本上,TBD研究有意义的生物个体单位的功能和结构,如大分子、基因、细胞核、细胞、血管和器官。所有功能都与确保可靠和有效的信息和能源交换的结构相绑定。结构的扰动导致功能的不太有效或完全丧失。分子生物学水平(大分子)上复杂的相互作用及其持续繁殖,除了需要复杂的生化分析系统外,还需要大量的计算。几乎所有可评估的信息都是可视化的或可以可视化的。因此,以复杂的方式应用图像内容分析可能是辅助人类图像解释甚至取代它的一个关键程序。图像内容信息包括可以从预定义的功能单元(对象)、它们的空间排列(结构)、图像转换之前或之后的像素派生特征(纹理)以及对象或基于像素的原语的句法组成(句法结构分析)中派生的信息。统计上显著的集群可以代表新的生物重要单位(例如,形成血管的特定(内皮)细胞的管状排列,不同性质的细胞的空间组成(细胞社会学)形成假定内源性凝集素参与的支气管[1])或其他新项目,如熵流程图和扩散密度。所有这些参数构成了一组强大的图像信息特征。由于其特定的临床意义(疾病关联),它们可以被认为是相互独立的,独立计算。
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Digital Pathology: How Far Are We from Automated Tissue-Based Diagnosis?
Basically, TBD investigates the function and structures of biological meaningful individual units, such as macromolecules, genes, nuclei, cells, vessels, and organs. All functions are bound to structures that ensure reliable and effective information and energy exchange. Disturbance of structures induces less effective or complete loss of functions. The complex interactions at molecular biological level (macromolecules) and their continuous reproduction require extensive computations in addition to the sophisticated biochemical analysis systems. Nearly all assessable information is of visual nature or can be visualized. Thus, image content analysis applied in a sophisticated manner might be one key procedure to assist human image interpretation or to even replace it. Image content information includes information that can be derived from predefined functional units (objects), their spatial arrangement (structure), pixel derived features prior of after image transformations (texture), and syntactic compositions of objects or of pixel based primitives (syntactic structure analysis). Statistically significant clusters can represent either new biological significant units (e.g., tubular arrangement of specific (endothelial) cells forming a vessel, spatial composition of cells of different nature (cellular sociology) forming a bronchus with assumed participation of endogenous lectins [1]) or other new items such as entropy flow charts and diffusion densities. All these parameters form a powerful set of image information features. They can be considered to be independent from each other and calculated independently for their specific clinical significance (disease association).
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