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一种用于免疫组织化学标记的组织切片自动分割、定量和比较分析的新计算方法。

A novel computational method for automatic segmentation, quantification and comparative analysis of immunohistochemically labeled tissue sections.

机构信息

Department of Computer Science "Giovanni Degli Antoni", Università degli Studi di Milano, Via Celoria 18, 20135, Milan, Italy.

Unit of Immunotherapy of Human Tumors, Department of Experimental Oncology and Molecular Medicine, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy.

出版信息

BMC Bioinformatics. 2018 Oct 15;19(Suppl 10):357. doi: 10.1186/s12859-018-2302-3.

DOI:10.1186/s12859-018-2302-3
PMID:30367588
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6191943/
Abstract

BACKGROUND

In the clinical practice, the objective quantification of histological results is essential not only to define objective and well-established protocols for diagnosis, treatment, and assessment, but also to ameliorate disease comprehension.

SOFTWARE

The software MIAQuant_Learn presented in this work segments, quantifies and analyzes markers in histochemical and immunohistochemical images obtained by different biological procedures and imaging tools. MIAQuant_Learn employs supervised learning techniques to customize the marker segmentation process with respect to any marker color appearance. Our software expresses the location of the segmented markers with respect to regions of interest by mean-distance histograms, which are numerically compared by measuring their intersection. When contiguous tissue sections stained by different markers are available, MIAQuant_Learn aligns them and overlaps the segmented markers in a unique image enabling a visual comparative analysis of the spatial distribution of each marker (markers' relative location). Additionally, it computes novel measures of markers' co-existence in tissue volumes depending on their density.

CONCLUSIONS

Applications of MIAQuant_Learn in clinical research studies have proven its effectiveness as a fast and efficient tool for the automatic extraction, quantification and analysis of histological sections. It is robust with respect to several deficits caused by image acquisition systems and produces objective and reproducible results. Thanks to its flexibility, MIAQuant_Learn represents an important tool to be exploited in basic research where needs are constantly changing.

摘要

背景

在临床实践中，客观地量化组织学结果不仅对于定义客观且成熟的诊断、治疗和评估方案至关重要，而且对于改善疾病认知也非常重要。

软件

本文介绍的软件 MIAQuant_Learn 可用于对通过不同生物程序和成像工具获得的组织化学和免疫组织化学图像进行分割、定量和分析标记物。MIAQuant_Learn 采用有监督的学习技术，根据任何标记物颜色的外观来定制标记物的分割过程。我们的软件通过平均距离直方图来表示分割标记物相对于感兴趣区域的位置，通过测量它们的交点来对其进行数值比较。当有连续的组织切片用不同的标记物染色时，MIAQuant_Learn 可以对它们进行对齐，并在一个独特的图像中重叠分割标记物，从而实现对每个标记物（标记物的相对位置）的空间分布的直观比较分析。此外，它还根据标记物的密度计算组织体积中标记物共存的新度量。

结论

MIAQuant_Learn 在临床研究中的应用证明了其作为一种快速有效的自动提取、定量和分析组织切片的工具的有效性。它对图像采集系统造成的多种缺陷具有鲁棒性，并且产生客观且可重复的结果。由于其灵活性，MIAQuant_Learn 是基础研究中需要不断变化的重要工具。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/53de/6191943/a677d544e239/12859_2018_2302_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/53de/6191943/5cf341d01d3f/12859_2018_2302_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/53de/6191943/538625d720d4/12859_2018_2302_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/53de/6191943/09fa8f3111a4/12859_2018_2302_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/53de/6191943/356b5084c1c2/12859_2018_2302_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/53de/6191943/657a3c9a54b2/12859_2018_2302_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/53de/6191943/78f5fe737529/12859_2018_2302_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/53de/6191943/a0a08cca4591/12859_2018_2302_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/53de/6191943/a677d544e239/12859_2018_2302_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/53de/6191943/5cf341d01d3f/12859_2018_2302_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/53de/6191943/538625d720d4/12859_2018_2302_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/53de/6191943/09fa8f3111a4/12859_2018_2302_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/53de/6191943/356b5084c1c2/12859_2018_2302_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/53de/6191943/657a3c9a54b2/12859_2018_2302_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/53de/6191943/78f5fe737529/12859_2018_2302_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/53de/6191943/a0a08cca4591/12859_2018_2302_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/53de/6191943/a677d544e239/12859_2018_2302_Fig8_HTML.jpg

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