Evaluation of linear discriminant analysis for automated Raman histological mapping of esophageal high-grade dysplasia
Evaluation of linear discriminant analysis for automated Raman histological mapping of esophageal high-grade dysplasia
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DOI：
10.1117/1.3512244
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发表时间：
2010-11-01
影响因子：
3.5
通讯作者：
Stone, Nicholas
中科院分区：
文献类型：
--
作者：
Hutchings, Joanne;Kendall, Catherine;Stone, Nicholas
快速的拉曼成像技术有可能用于组织病理学的自动诊断，为组织学诊断提供一种辅助技术。这项工作的目的是评估的可行性自动和客观的病理分类的拉曼地图使用线性判别分析。获取食管组织切片的拉曼图。使用拉曼图谱数据（6483个光谱）的子集进行主成分（PC）馈入线性判别分析（LDA）。获得了97.7%的总体（经验证的）训练分类模型性能（灵敏度95.0至100%，特异性98.6至100%）。其余的图谱光谱（131，672个光谱）被投影到分类模型上，产生拉曼图像，证明与连续的苏木素和伊红（HE）切片具有良好的相关性。初步结果表明，LDA有潜力自动病理诊断的食管拉曼图像，但由于测试光谱的分类被迫到现有的训练组，需要进一步的工作来优化训练模型。小的像素尺寸对于使用映射数据开发训练数据集是有利的，尽管由于获得了额外的形态学信息而需要较长的映射时间，并且可以促进将来进一步组织组（例如基底细胞/固有层）的分化，但是较大的像素尺寸（和更快的映射）对于临床应用可能更可行。(C)2010年，美国光学仪器工程师学会（Society of Photo-Optical Instrumentation Engineers）[DOI：10.1117/1.3512244]
Rapid Raman mapping has the potential to be used for automated histopathology diagnosis, providing an adjunct technique to histology diagnosis. The aim of this work is to evaluate the feasibility of automated and objective pathology classification of Raman maps using linear discriminant analysis. Raman maps of esophageal tissue sections are acquired. Principal component (PC)-fed linear discriminant analysis (LDA) is carried out using subsets of the Raman map data (6483 spectra). An overall (validated) training classification model performance of 97.7% (sensitivity 95.0 to 100% and specificity 98.6 to 100%) is obtained. The remainder of the map spectra (131,672 spectra) are projected onto the classification model resulting in Raman images, demonstrating good correlation with contiguous hematoxylin and eosin (HE) sections. Initial results suggest that LDA has the potential to automate pathology diagnosis of esophageal Raman images, but since the classification of test spectra is forced into existing training groups, further work is required to optimize the training model. A small pixel size is advantageous for developing the training datasets using mapping data, despite lengthy mapping times, due to additional morphological information gained, and could facilitate differentiation of further tissue groups, such as the basal cells/lamina propria, in the future, but larger pixels sizes (and faster mapping) may be more feasible for clinical application. (C) 2010 Society of Photo-Optical Instrumentation Engineers. [DOI: 10.1117/1.3512244]