Artificial neural networks as supervised techniques for FT-IR microspectroscopic imaging
Artificial neural networks as supervised techniques for FT-IR microspectroscopic imaging
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DOI：
10.1002/cem.993
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发表时间：
2006-05-01
影响因子：
2.4
通讯作者：
Naumann, Dieter
中科院分区：
文献类型：
--
作者：
Lasch, Peter;Diem, Max;Naumann, Dieter
在本报告中，证明了一种改进的组织学标本红外显微光谱数据图像分割方法的适用性。傅里叶变换红外（FT-IR）显微光谱用于记录人类结直肠腺癌的高光谱数据集，并建立空间分辨组织光谱的数据库。该结肠显微光谱数据库包含来自 28 名患者样本和 12 种不同组织学结构的 4120 个高质量 FT-IR 点光谱。数据库中包含的光谱信息用于教导和验证多层感知器人工神经网络 (MLP-ANN) 模型。然后将这些分类模型用于数据库分析，并用于从完整的 FT-IR 显微光谱组织图生成假彩色图像。这项研究的一个重要方面还在于证明如何具体优化诊断敏感性和特异性。举例说明，可以通过改变每班教学模式的数量来修改这两个相互关联的测试参数。人工神经网络拓扑的定义对于实现组织病理学黄金标准和红外光谱之间的高度对应至关重要。特别是，ANN 分类的分层方案被证明对于组织光谱的可靠分类具有优越性。研究发现，无监督聚类方法，特别是凝聚层次聚类（AHC），在模型生成的初始阶段很有帮助。如果通过考虑聚类分析提供的分类信息来进行 ANN 的类定义，则可以获得最佳分类结果。版权所有 (C) 2007 John Wiley & Sons, Ltd.
In this report the applicability of an improved method of image segmentation of infrared microspectroscopic data from histological specimens is demonstrated. Fourier transform infrared (FT-IR) microspectroscopy was used to record hyperspectral data sets from human colorectal adenocarcinomas and to build up a database of spatially resolved tissue spectra. This database of colon microspectra comprised 4120 high-quality FT-IR point spectra from 28 patient samples and 12 different histological structures. The spectral information contained in the database was employed to teach and validate multilayer perceptron artificial neural network (MLP-ANN) models. These classification models were then employed for database analysis and utilised to produce false colour images from complete tissue maps of FT-IR microspectra. An important aspect of this study was also to demonstrate how the diagnostic sensitivity and specificity can be specifically optimised. An example is given which shows that changes of the number of teaching patterns per class can be used to modify these two interrelated test parameters. The definition of ANN topology turned out to be crucial to achieve a high degree of correspondence between the gold standard of histopathology and IR spectroscopy. Particularly, a hierarchical scheme of ANN classification proved to be superior for the reliable classification of tissue spectra. It was found that unsupervised methods of clustering, specifically agglomerative hierarchical clustering (AHC), were helpful in the initial phases of model generation. Optimal classification results could be achieved if the class definitions for the ANNs were carried out by considering the classification information provided by cluster analysis. Copyright (C) 2007 John Wiley & Sons, Ltd.