Takeyama Saori, Watanabe Tomoaki, Gong Nanxin, Yamaguchi Masahiro, Urata Takumi, Kimura Fumikazu, Ishii Keiko
Tokyo Institute of Technology, School of Engineering, Department of Information and Communications Engineering, Yokohama, Japan.
Shinshu University, School of Health Sciences, Department of Biomedical Laboratory Sciences, Matsumoto, Japan.
J Med Imaging (Bellingham). 2025 Jan;12(1):017501. doi: 10.1117/1.JMI.12.1.017501. Epub 2024 Dec 28.
The color of Papanicolaou-stained specimens is a crucial feature in cytology diagnosis. However, the quantification of color using digital images is challenging due to the variations in the staining process and characteristics of imaging equipment. The dye amount estimation of stained specimens is helpful for quantitatively interpreting the color based on a physical model. It has been realized with color unmixing and applied to staining with three or fewer dyes. Nevertheless, the Papanicolaou stain comprises five dyes. Thus, we employ multispectral imaging with more channels for quantitative analysis of the Papanicolaou-stained cervical cytology samples.
We estimate the dye amount map from a 14-band multispectral observation capturing a Papanicolaou-stained specimen using the actual measured spectral characteristics of the single-stained samples. The estimated dye amount maps were employed for the quantitative interpretation of the color of cytoplasmic mucin of lobular endocervical glandular hyperplasia (LEGH) and normal endocervical (EC) cells in a uterine cervical lesion.
We demonstrated the dye amount estimation performance of the proposed method using single-stain images and Papanicolaou-stain images. Moreover, the yellowish color in the LEGH cells is found to be interpreted with more orange G (OG) and less Eosin Y (EY) dye amounts. We also elucidated that LEGH and EC cells could be classified using linear classifiers from the dye amount.
Multispectral imaging enables the quantitative analysis of dye amount maps of Papanicolaou-stained cytology specimens. The effectiveness is demonstrated in interpreting and classifying the cytoplasmic mucin of EC and LEGH cells in cervical cytology.
巴氏染色标本的颜色是细胞学诊断的关键特征。然而,由于染色过程和成像设备特性的差异,利用数字图像对颜色进行量化具有挑战性。染色标本的染料量估计有助于基于物理模型对颜色进行定量解释。通过颜色分解已经实现了这一点,并应用于三种或更少染料的染色。然而,巴氏染色包含五种染料。因此,我们采用具有更多通道的多光谱成像对巴氏染色的宫颈细胞学样本进行定量分析。
我们利用单染色样本的实际测量光谱特征,从捕获巴氏染色标本的14波段多光谱观测中估计染料量图。估计的染料量图用于对子宫颈病变中小叶型宫颈腺增生(LEGH)和正常宫颈内膜(EC)细胞的细胞质粘蛋白颜色进行定量解释。
我们使用单染色图像和巴氏染色图像展示了所提出方法的染料量估计性能。此外,发现LEGH细胞中的淡黄色可以用更多的橙色G(OG)染料量和更少的伊红Y(EY)染料量来解释。我们还阐明,可以使用基于染料量的线性分类器对LEGH和EC细胞进行分类。
多光谱成像能够对巴氏染色细胞学标本的染料量图进行定量分析。在解释和分类宫颈细胞学中EC和LEGH细胞的细胞质粘蛋白方面证明了其有效性。