Saitou Takashi, Takanezawa Sota, Ninomiya Hiroko, Watanabe Takao, Yamamoto Shin, Hiasa Yoichi, Imamura Takeshi
Department of Molecular Medicine for Pathogenesis, Graduate School of Medicine, Ehime University, Toon, Japan.
Translational Research Center, Ehime University Hospital, Toon, Japan.
Front Med (Lausanne). 2018 Dec 11;5:350. doi: 10.3389/fmed.2018.00350. eCollection 2018.
Tissue intrinsic emission fluorescence provides useful diagnostic information for various diseases. Because of its unique feature of spectral profiles depending on tissue types, spectroscopic imaging is a promising tool for accurate evaluation of endogenous fluorophores. However, due to difficulties in discriminating those sources, quantitative analysis remains challenging. In this study, we quantitatively investigated spectral-spatial features of multi-photon excitation fluorescence in normal and diseased livers. For morphometrics of multi-photon excitation spectra, we examined a marker-controlled segmentation approach and its application to liver fibrosis assessment by employing a mouse model of carbon tetrachloride (CCl)-induced liver fibrosis. We formulated a procedure of internal marker selection where markers were chosen to reflect typical biochemical species in the liver, followed by image segmentation and local morphological feature extraction. Image segmentation enabled us to apply mathematical morphology analysis, and the local feature was applied to the automated classification test based on a machine learning framework, both demonstrating highly accurate classifications. Through the analyses, we showed that spectral imaging of native fluorescence from liver tissues have the capability of differentiating not only between normal and diseased, but also between progressive disease states. The proposed approach provides the basics of spectroscopy-based digital histopathology of chronic liver diseases, and can be applied to a range of diseases associated with autofluorescence alterations.
组织固有发射荧光可为各种疾病提供有用的诊断信息。由于其光谱特征取决于组织类型的独特特性，光谱成像对于准确评估内源性荧光团是一种很有前景的工具。然而，由于难以区分这些来源，定量分析仍然具有挑战性。在本研究中，我们定量研究了正常和患病肝脏中多光子激发荧光的光谱空间特征。对于多光子激发光谱的形态计量学，我们通过使用四氯化碳（CCl）诱导的肝纤维化小鼠模型，研究了一种标记物控制的分割方法及其在肝纤维化评估中的应用。我们制定了一种内部标记物选择程序，选择标记物以反映肝脏中的典型生化物质，然后进行图像分割和局部形态特征提取。图像分割使我们能够应用数学形态学分析，并且局部特征被应用于基于机器学习框架的自动分类测试，两者都显示出高度准确的分类。通过分析，我们表明肝脏组织天然荧光的光谱成像不仅能够区分正常和患病状态，还能够区分疾病的进展状态。所提出的方法为基于光谱学的慢性肝病数字组织病理学提供了基础，并且可以应用于一系列与自发荧光改变相关的疾病。
