首页> 美国卫生研究院文献>Frontiers in Oncology >Whole-Section Tumor Micro-Architecture Analysis by a Two-Dimensional Phasor-Based Approach Applied to Polarization-Dependent Second Harmonic Imaging
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Whole-Section Tumor Micro-Architecture Analysis by a Two-Dimensional Phasor-Based Approach Applied to Polarization-Dependent Second Harmonic Imaging

机译:基于二维相量的全截面肿瘤微结构分析,用于偏振相关的二次谐波成像

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摘要

Second Harmonic Generation (SHG) microscopy has gained much interest in the histopathology field since it allows label-free imaging of tissues simultaneously providing information on their morphology and on the collagen microarchitecture, thereby highlighting the onset of pathologies and diseases. A wide request of image analysis tools is growing, with the aim to increase the reliability of the analysis of the huge amount of acquired data and to assist pathologists in a user-independent way during their diagnosis. In this light, we exploit here a set of phasor-parameters that, coupled to a 2-dimensional phasor-based approach (μMAPPS, Microscopic Multiparametric Analysis by Phasor projection of Polarization-dependent SHG signal) and a clustering algorithm, allow to automatically recover different collagen microarchitectures in the tissues extracellular matrix. The collagen fibrils microscopic parameters (orientation and anisotropy) are analyzed at a mesoscopic level by quantifying their local spatial heterogeneity in histopathology sections (few mm in size) from two cancer xenografts in mice, in order to maximally discriminate different collagen organizations, allowing in this case to identify the tumor area with respect to the surrounding skin tissue. We show that the “fibril entropy” parameter, which describes the tissue order on a selected spatial scale, is the most effective in enlightening the tumor edges, opening the possibility of their automatic segmentation. Our method, therefore, combined with tissue morphology information, has the potential to become a support to standard histopathology in diseases diagnosis.
机译:第二谐波产生(SHG)显微镜在组织病理学领域引起了极大的兴趣,因为它允许组织的无标记成像同时提供有关其形态和胶原微结构的信息,从而突出了病理和疾病的发作。对图像分析工具的广泛需求正在增长,目的是提高对大量采集数据的分析的可靠性,并在诊断过程中以独立于用户的方式帮助病理学家。有鉴于此,我们在这里利用一组相量参数,再结合基于二维相量的方法(μMAPPS,通过偏振相关SHG信号的相量投影进行微观多参数分析)和一种聚类算法,可以自动恢复组织细胞外基质中的不同胶原微结构。通过在小鼠的两个癌异种移植物中的组织病理学切片(几毫米大小)中量化它们的局部空间异质性,在介观水平上分析胶原原纤维的微观参数(方向和各向异性),以便最大程度地区分不同的胶原组织。这种情况可以确定相对于周围皮肤组织的肿瘤区域。我们表明,“原纤维熵”参数(在选定的空间尺度上描述组织顺序)在启发肿瘤边缘方面最有效,从而打开了自动分割的可能性。因此,我们的方法结合组织形态学信息,有可能成为疾病诊断中标准组织病理学的支持。

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