基于染色光谱对病理切片进行高光谱分析以检测胰腺肿瘤细胞核。
Detection of pancreatic tumor cell nuclei via a hyperspectral analysis of pathological slides based on stain spectra.
作者信息
Ishikawa Masahiro, Okamoto Chisato, Shinoda Kazuma, Komagata Hideki, Iwamoto Chika, Ohuchida Kenoki, Hashizume Makoto, Shimizu Akinobu, Kobayashi Naoki
机构信息
Saitama Medical University, Faculty of Health & Medical Care, Yamane-1397-1, Hidaka-shi, 350-1241, Japan.
Graduate School of Engineering, Utsunomiya University, 7-1-2 Yoto, Utsunomiya, Tochigi 321-8585, Japan.
出版信息
Biomed Opt Express. 2019 Aug 9;10(9):4568-4588. doi: 10.1364/BOE.10.004568. eCollection 2019 Sep 1.
Hyperspectral imaging (HSI) provides more detailed information than red-green-blue (RGB) imaging, and therefore has potential applications in computer-aided pathological diagnosis. This study aimed to develop a pattern recognition method based on HSI, called hyperspectral analysis of pathological slides based on stain spectrum (HAPSS), to detect cancers in hematoxylin and eosin-stained pathological slides of pancreatic tumors. The samples, comprising hyperspectral cubes of 420-750 nm, were harvested for HSI and tissue microarray (TMA) analysis. As a result of conducting HAPSS experiments with a support vector machine (SVM) classifier, we obtained maximal accuracy of 94%, a 14% improvement over the widely used RGB images. Thus, HAPSS is a suitable method to automatically detect tumors in pathological slides of the pancreas.
高光谱成像（HSI）比红绿蓝（RGB）成像提供更详细的信息，因此在计算机辅助病理诊断中具有潜在应用。本研究旨在开发一种基于HSI的模式识别方法，称为基于染色光谱的病理切片高光谱分析（HAPSS），以检测胰腺肿瘤苏木精和伊红染色病理切片中的癌症。采集了包含420 - 750 nm高光谱立方体的样本，用于HSI和组织微阵列（TMA）分析。通过使用支持向量机（SVM）分类器进行HAPSS实验，我们获得了94%的最大准确率，比广泛使用的RGB图像提高了14%。因此，HAPSS是一种适用于自动检测胰腺病理切片中肿瘤的方法。
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