Experimental Assessment of Color Deconvolution and Color Normalization for Automated Classification of Histology Images Stained with Hematoxylin and Eosin.
Experimental Assessment of Color Deconvolution and Color Normalization for Automated Classification of Histology Images Stained with Hematoxylin and Eosin.
复制标题
DOI：
10.3390/cancers12113337
复制
发表时间：
2020-11-11
期刊：
影响因子：
5.2
通讯作者：
Reyes-Aldasoro CC
中科院分区：
文献类型：
--
作者：
Bianconi F;Kather JN;Reyes-Aldasoro CC
由于试剂、染色条件、制备程序和获取系统的变化，H&E染色的组织学图像的外观可能会有很大的变化。在这项工作中，我们研究了颜色预处理-特别是颜色反卷积和颜色归一化-是否可以用来纠正这种可变性并提高自动分类过程的性能。通过对11个数据集、13个图像描述符和8种颜色预处理方法的实验，我们发现在大多数情况下，不进行颜色预处理是最好的选择。组织学评价在肿瘤的诊断和治疗中起着重要作用。由于试剂、染色条件、制备程序和图像采集系统等几个因素的差异，h&e染色图像的外观可能会有很大差异。这些潜在的噪声源都可能对计算机辅助分类产生负面影响。为了尽量减少这些伪影及其潜在的负面影响，文献中提出了几种颜色预处理方法，例如，颜色增强，颜色恒定，颜色反卷积和颜色转移。然而，很少有人在定量的基础上调查这些方法的功效。在本文中，我们评估了颜色恒定性、反卷积和转移对代表不同类型癌症的h&e染色图像的自动分类的影响，特别是乳腺癌、前列腺癌、结直肠癌和恶性淋巴瘤。我们的研究结果表明，在大多数情况下，颜色预处理并不能提高分类精度，特别是当与基于颜色的图像描述符相结合时。然而，一些预处理方法在与一些基于纹理的方法（如Gabor过滤器和局部二进制模式）一起使用时是有益的。
The appearance of histology images stained with H&E can vary a lot as a consequence of changes in the reagents, staining conditions, preparation procedure and acquisition system. In this work we investigated whether color preprocessing—specifically color deconvolution and color normalization—could be used to correct such variability and improve the performance of automated classification procedures. Experimenting on 11 datasets, 13 image descriptors and eight color pre-processing methods we found that doing no color preprocessing was the best option in most cases. Histological evaluation plays a major role in cancer diagnosis and treatment. The appearance of H&E-stained images can vary significantly as a consequence of differences in several factors, such as reagents, staining conditions, preparation procedure and image acquisition system. Such potential sources of noise can all have negative effects on computer-assisted classification. To minimize such artefacts and their potentially negative effects several color pre-processing methods have been proposed in the literature—for instance, color augmentation, color constancy, color deconvolution and color transfer. Still, little work has been done to investigate the efficacy of these methods on a quantitative basis. In this paper, we evaluated the effects of color constancy, deconvolution and transfer on automated classification of H&E-stained images representing different types of cancers—specifically breast, prostate, colorectal cancer and malignant lymphoma. Our results indicate that in most cases color pre-processing does not improve the classification accuracy, especially when coupled with color-based image descriptors. Some pre-processing methods, however, can be beneficial when used with some texture-based methods like Gabor filters and Local Binary Patterns.
登录
查看更多内容
影响因子：
4.6
作者：
Bychkov D;Linder N;Turkki R;Nordling S;Kovanen PE;Verrill C;Walliander M;Lundin M;Haglund C;Lundin J
通讯作者：
Lundin J
影响因子：
78.8
作者：
Bera, Kaustav;Schalper, Kurt A.;Madabhushi, Anant
通讯作者：
Madabhushi, Anant
影响因子：
1.1
作者：
Bianconi, Francesco;Bello-Cerezo, Raquel;Napoletano, Paolo
通讯作者：
Napoletano, Paolo
DOI：
10.1364/josaa.33.000017
发表时间：
2016-01-01
影响因子：
1.9
作者：
Cusano, Claudio;Napoletano, Paolo;Schettini, Raimondo
通讯作者：
Schettini, Raimondo
影响因子：
3.9
作者：
Hameed, Zabit;Zahia, Sofia;Maria Vanegas, Ana
通讯作者：
Maria Vanegas, Ana