Nguyen Thi Nguyet Que, Jeannesson Pierre, Groh Audrey, Piot Olivier, Guenot Dominique, Gobinet Cyril
Université de Reims Champagne-Ardenne, Equipe MéDIAN-Biophotonique et Technologies pour la Santé, UFR de Pharmacie, Reims, France.
CNRS UMR7369, Matrice Extracellulaire et Dynamique Cellulaire (MEDyC), Reims, France.
J Biophotonics. 2016 May;9(5):521-32. doi: 10.1002/jbio.201500285. Epub 2016 Feb 12.
In label-free Fourier-transform infrared histology, spectral images are individually recorded from tissue sections, pre-processed and clustered. Each single resulting color-coded image is annotated by a pathologist to obtain the best possible match with tissue structures revealed after Hematoxylin-Eosin staining. However, the main limitations of this approach are the empirical choice of the number of clusters in unsupervised classification, and the marked color heterogeneity between the clustered spectral images. Here, using normal murine and human colon tissues, we developed an automatic multi-image spectral histology to simultaneously analyze a set of spectral images (8 images mice samples and 72 images human ones). This procedure consisted of a joint Extended Multiplicative Signal Correction (EMSC) to numerically deparaffinize the tissue sections, followed by an automated joint K-Means (KM) clustering using the hierarchical double application of Pakhira-Bandyopadhyay-Maulik (PBM) validity index. Using this procedure, the main murine and human colon histological structures were correctly identified at both the intra- and the inter-individual levels, especially the crypts, secreted mucus, lamina propria and submucosa. Here, we show that batched multi-image spectral histology procedure is insensitive to the reference spectrum but highly sensitive to the paraffin model of joint EMSC. In conclusion, combining joint EMSC and joint KM clustering by double PBM application allows to achieve objective and automated batched multi-image spectral histology.
在无标记傅里叶变换红外组织学中，光谱图像是从组织切片中单独记录、预处理并聚类的。每个得到的单一彩色编码图像都由病理学家进行注释，以使其与苏木精 - 伊红染色后显示的组织结构达到最佳匹配。然而，这种方法的主要局限性在于无监督分类中聚类数量的经验性选择，以及聚类光谱图像之间明显的颜色异质性。在此，我们使用正常小鼠和人类结肠组织，开发了一种自动多图像光谱组织学方法，以同时分析一组光谱图像（小鼠样本8张图像，人类样本72张图像）。该过程包括联合扩展乘法信号校正（EMSC）以对组织切片进行数值脱石蜡处理，随后使用Pakhira - Bandyopadhyay - Maulik（PBM）有效性指数的分层双重应用进行自动联合K均值（KM）聚类。使用该方法，主要的小鼠和人类结肠组织结构在个体内和个体间水平都能被正确识别，尤其是隐窝、分泌的黏液、固有层和黏膜下层。在此，我们表明批量多图像光谱组织学方法对参考光谱不敏感，但对联合EMSC的石蜡模型高度敏感。总之，通过双重应用PBM将联合EMSC和联合KM聚类相结合，能够实现客观且自动化的批量多图像光谱组织学。
