如何定义和实施基于组织的诊断辅助(外科病理学):调查

K. Kayser, S. Borkenfeld, Rita Carvalho, G. Kayser
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引用次数: 0

摘要

数字病理学已开始进入组织诊断领域。它提供了几种应用,特别是在常规外科病理(基于组织的诊断)的帮助。诊断助手是帮助病理学家进行常规诊断工作的程序。本文描述了如何设计合适的算法。理论:基于组织的诊断来源于a)图像内容信息,b)临床病史,c)病理学家的专业知识,d)对疾病的了解。它可以转化为统计决策算法(神经网络、判别分析、因子分析等)。。图像内容信息:对图像内容信息(ICI)的分析有助于不同层次的医学诊断。该水平取决于潜在疾病(诊断)和衍生的潜在治疗。预分析算法包括a)图像标准化(阴影、放大、灰度值分布)和感兴趣区域(ROI)的评估。ICI嵌入在三个坐标中(纹理、对象、结构)。对物体和结构的分析需要外部知识(细胞、神经、血管、树、人……)。纹理完全基于像素,独立于外部知识[1,2]。算法:立体、句法结构分析和物体特征测量(面积、周长、力矩等)是有用的工具,结合外部知识和适当的形象标准化。结构和纹理参数需要邻域的定义(Voronoi, O'Caliaghan)。纹理特征基于模拟时间序列分析的算法,有助于ROI定义和疾病分类[1,2]。材料:通过相同的算法,从包含不同器官(结肠、肺、胸膜、胃、甲状腺)的大组组织学图像中自动评估了粗糙的诊断(约1000例)。试验的结果是可重复和正确的分类(90% - 98%)。结论:应用的算法可以结合起来构建高效的诊断助手。它们可以扩展到辅助更有区别的诊断(包括特定的染色、临床病史等)。。它们可以用来形成“图像信息”的一般理论。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
How to define and implement diagnosis assistants in tissue-based diagnosis (surgical pathology): A survey
Digital pathology has started to enter the field of tissue - based diagnosis. It offers several applications, especially assistance in routine surgical pathology (tissue - based diagnosis). Diagnosis assistants are programs that assist the routine diagnosis work of a pathologist. Herein we describe how to appropriate design suitable algorithms. Theory: Tissue - based diagnoses derives from a) image content information, b) clinical history, c) expertise of the pathologist, d) knowledge about the disease. It can be transferred to a statistical decision algorithm (neural network, discriminate analysis, factor analysis, ... ). Image content information: Analysis of image content information (ICI) can contribute to medical diagnosis at different levels. The level depends upon the underlying disease (diagnosis) and the derived potential treatment. Pre - analysis algorithms include a) image standardization (shading, magnification, grey value distribution), and evaluation of regions of interest (ROI). ICI is embedded in three coordinates (texture, object, structure). Analysis of objects and structure require external knowledge (cell, nerve, vessel, tree, man, ... ). Texture is solely pixel - based and independent from external knowledge [1,2]. Algorithms: Stereology, syntactic structure analysis and measurement of object features (area, circumference, moments, ... ) are useful tools in combination with external knowledge and appropriate image standardization. Structure and texture parameters require the definition of neighbourhood (Voronoi, O'Caliaghan). Texture features are based upon algorithms that mimic time series analysis and can contribute to ROI definition and to disease classification [1, 2]. Material: Crude diagnoses have been automatically evaluated by the same algorithm from large sets of histological images comprising different organs (colon, lung, pleura, stomach, thyroid (> 1,000 cases). The trials resulted in a reproducible and correct classification (90 - 98 %). Conclusions: The applied algorithms can be combined to construct efficient diagnosis assistants. They can be extended to assistants of more differentiated diagnoses (inclusion of specific stains, clinical history, etc ... ). They can serve to formulate a general theory of "image information".
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