Toward digital staining using stimulated Raman scattering and statistical machine learning

K. Tanji; Y. Otsuka; S. Satoh; H. Hashimoto; Y. Ozeki; Kazuyoshi Itoh

doi:10.1117/12.2042820

20 March 2014 Toward digital staining using stimulated Raman scattering and statistical machine learning

K. Tanji, Y. Otsuka, S. Satoh, H. Hashimoto, Y. Ozeki, Kazuyoshi Itoh

Proceedings Volume 9041, Medical Imaging 2014: Digital Pathology; 90410H (2014) https://doi.org/10.1117/12.2042820
Event: SPIE Medical Imaging, 2014, San Diego, California, United States

Abstract

Stimulated Raman scattering (SRS) spectral microscopy is a promising imaging method, based on vibrational spectroscopy, which can visualize biological tissues with chemical specificity. SRS spectral microscopy has been used to obtain two-dimensional spectral images of rat liver tissue, three-dimensional images of a vessel in rat liver, and in vivo spectral images of mouse ear skin. Various multivariate analysis techniques, such as principal component analysis and independent component analysis, have been used to obtain spectral images. In this study, we propose a digital staining method. This method uses SRS spectra and statistical machine learning that makes use of prior knowledge of spectral peaks and their two-dimensional distributional patterns corresponding to the composition of tissue samples. The method selects spectral peaks on the basis of Mahalanobis distance, which is defined as the ratio of inter-group variation to intragroup variation. We also make use of higher-order local autocorrelations as feature values for two-dimensional distributional patterns. This combination of techniques allows groups corresponding to different intracellular structures to be clearly discriminated in the multidimensional feature space. We investigate the performance of our method on mouse liver tissue samples and show that the proposed method can digitally stain each intracellular structure such as cell nuclei, cytoplasm, and erythrocytes separately and clearly without time-consuming chemical staining processes. We anticipate that our method could be applied to computer-aided pathological diagnosis.

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K. Tanji, Y. Otsuka, S. Satoh, H. Hashimoto, Y. Ozeki, and Kazuyoshi Itoh "Toward digital staining using stimulated Raman scattering and statistical machine learning", Proc. SPIE 9041, Medical Imaging 2014: Digital Pathology, 90410H (20 March 2014); https://doi.org/10.1117/12.2042820

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KEYWORDS

Tissues

Machine learning

Mahalanobis distance

Liver

Raman scattering

Microscopy

Statistical analysis

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