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图像分析中的偏差及其解决方案:无偏体视学

Bias in image analysis and its solution: unbiased stereology.

作者信息

Brown Danielle L

机构信息

Charles River Laboratories, Pathology Associates, 4025 Stirrup Creek Drive, Suite 150, Durham, NC 27703, USA.

出版信息

J Toxicol Pathol. 2017 Jul;30(3):183-191. doi: 10.1293/tox.2017-0013. Epub 2017 Mar 4.

Abstract

Although the human eye is excellent for pattern recognition, it often lacks the sensitivity to detect subtle changes in particle density. Because of this, quantitative evaluation may be required in some studies. A common type of quantitative assessment used for routine toxicology studies is two-dimensional histomorphometry. Although this technique can provide additional information about the tissue section being examined, it does not give information about the tissue as a whole. Furthermore, it produces biased (inaccurate) data that does not take into account the size, shape, or orientation of particles. In contrast, stereology is a technique that utilizes stringent sampling methods to obtain three-dimensional information about the entire tissue that is unbiased. The purpose of this review is to illuminate the sources of bias with two-dimensional morphometry, how it can affect the data, and how that bias is minimized with stereology.

摘要

尽管人眼在模式识别方面表现出色,但它往往缺乏检测颗粒密度细微变化的灵敏度。因此,在某些研究中可能需要进行定量评估。用于常规毒理学研究的一种常见定量评估类型是二维组织形态计量学。尽管该技术可以提供有关所检查组织切片的额外信息,但它无法提供有关整个组织的信息。此外,它会产生有偏差(不准确)的数据,没有考虑颗粒的大小、形状或方向。相比之下,体视学是一种利用严格抽样方法获取有关整个组织的无偏差三维信息的技术。本综述的目的是阐明二维形态计量学的偏差来源、它如何影响数据,以及如何通过体视学将该偏差最小化。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9856/5545670/364ab64f1fa0/tox-30-183-g001.jpg

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